Teleoperation system and method for trajectory modification of autonomous vehicles

ABSTRACT

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. More specifically, systems, devices, and methods are configured to initiate modification of trajectories to influence navigation of autonomous vehicles. In particular, a method may include receiving a teleoperation message via a communication link from an autonomous vehicle, detecting data from the teleoperation message specifying an event associated with the autonomous vehicle, identifying one or more courses of action to perform responsive to detecting the data specifying the event, and generating visualization data to present information associated with the event to a display of a teleoperator computing device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/932,966, filed Nov. 4, 2015 entitled “TELEOPERATION SYSTEM AND METHODFOR TRAJECTORY MODIFICATION OF AUTONOMOUS VEHICLES,” which is related toU.S. patent application Ser. No. 14/932,959 filed Nov. 4, 2015, entitled“AUTONOMOUS VEHICLE FLEET SERVICE AND SYSTEM,” U.S. patent applicationSer. No. 14/932,963 filed Nov. 4, 2015, entitled “ADAPTIVE MAPPING TONAVIGATE AUTONOMOUS VEHICLES RESPONSIVE TO PHYSICAL ENVIRONMENTCHANGES,” U.S. patent application Ser. No. 14/932,940 filed Nov. 4,2015, entitled “AUTOMATED EXTRACTION OF SEMANTIC INFORMATION TO ENHANCEINCREMENTAL MAPPING MODIFICATIONS FOR ROBOTIC VEHICLES,” U.S. patentapplication Ser. No. 14/756,995 filed Nov. 4, 2015, entitled“COORDINATION OF DISPATCHING AND MAINTAINING FLEET OF AUTONOMOUSVEHICLES,” U.S. patent application Ser. No. 14/756,992 filed Nov. 4,2015, entitled “ADAPTIVE AUTONOMOUS VEHICLE PLANNER LOGIC,” U.S. patentapplication Ser. No. 14/756,991 filed Nov. 4, 2015, entitled“SENSOR-BASED OBJECT DETECTION OPTIMIZATION FOR AUTONOMOUS VEHICLES,”and U.S. patent application Ser. No. 14/756,996 filed Nov. 4, 2015,entitled “CALIBRATION FOR AUTONOMOUS VEHICLE OPERATION,” all of whichare hereby incorporated by reference in their entirety for all purposes.

FIELD

Various embodiments relate generally to autonomous vehicles andassociated mechanical, electrical and electronic hardware, computersoftware and systems, and wired and wireless network communications toprovide an autonomous vehicle fleet as a service. More specifically,systems, devices, and methods are configured to initiate modification oftrajectories (e.g., remotely) to influence navigation of autonomousvehicles.

BACKGROUND

A variety of approaches to developing driverless vehicles focuspredominately on automating conventional vehicles (e.g., manually-drivenautomotive vehicles) with an aim toward producing driverless vehiclesfor consumer purchase. For example, a number of automotive companies andaffiliates are modifying conventional automobiles and controlmechanisms, such as steering, to provide consumers with an ability toown a vehicle that may operate without a driver. In some approaches, aconventional driverless vehicle performs safety-critical drivingfunctions in some conditions, but requires a driver to assume control(e.g., steering, etc.) should the vehicle controller fail to resolvecertain issues that might jeopardize the safety of the occupants.

Although functional, conventional driverless vehicles typically have anumber of drawbacks. For example, a large number of driverless carsunder development have evolved from vehicles requiring manual (i.e.,human-controlled) steering and other like automotive functions.Therefore, a majority of driverless cars are based on a paradigm that avehicle is to be designed to accommodate a licensed driver, for which aspecific seat or location is reserved within the vehicle. As such,driverless vehicles are designed sub-optimally and generally foregoopportunities to simplify vehicle design and conserve resources (e.g.,reducing costs of producing a driverless vehicle). Other drawbacks arealso present in conventional driverless vehicles.

Other drawbacks are also present in conventional transportationservices, which are not well-suited for managing, for example, inventoryof vehicles effectively due to the common approaches of providingconventional transportation and ride-sharing services. In oneconventional approach, passengers are required to access a mobileapplication to request transportation services via a centralized servicethat assigns a human driver and vehicle (e.g., under private ownership)to a passenger. With the use of differently-owned vehicles, maintenanceof private vehicles and safety systems generally go unchecked. Inanother conventional approach, some entities enable ride-sharing for agroup of vehicles by allowing drivers, who enroll as members, access tovehicles that are shared among the members. This approach is notwell-suited to provide for convenient transportation services as driversneed to pick up and drop off shared vehicles at specific locations,which typically are rare and sparse in city environments, and requireaccess to relatively expensive real estate (i.e., parking lots) at whichto park ride-shared vehicles. In the above-described conventionalapproaches, the traditional vehicles used to provide transportationservices are generally under-utilized, from an inventory perspective, asthe vehicles are rendered immobile once a driver departs. Further,ride-sharing approaches (as well as individually-owned vehicletransportation services) generally are not well-suited to rebalanceinventory to match demand of transportation services to accommodateusage and typical travel patterns. Note, too, that someconventionally-described vehicles having limited self-driving automationcapabilities also are not well-suited to rebalance inventories as ahuman driver generally may be required. Examples of vehicles havinglimited self-driving automation capabilities are vehicles designated asLevel 3 (“L3”) vehicles, according to the U.S. Department ofTransportation's National Highway Traffic Safety Administration(“NHTSA”).

As another drawback, typical approaches to driverless vehicles aregenerally not well-suited to detect and navigate vehicles relative tointeractions (e.g., social interactions) between a vehicle-in-travel andother drivers of vehicles or individuals. For example, some conventionalapproaches are not sufficiently able to identify pedestrians, cyclists,etc., and associated interactions, such as eye contact, gesturing, andthe like, for purposes of addressing safety risks to occupants of adriverless vehicles, as well as drivers of other vehicles, pedestrians,etc.

Thus, what is needed is a solution for implementing autonomous vehicles,without the limitations of conventional techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the invention aredisclosed in the following detailed description and the accompanyingdrawings:

FIG. 1 is a diagram depicting implementation of a fleet of autonomousvehicles that are communicatively networked to an autonomous vehicleservice platform, according to some embodiments;

FIG. 2 is an example of a flow diagram to monitor a fleet of autonomousvehicles, according to some embodiments;

FIG. 3A is a diagram depicting examples of sensors and other autonomousvehicle components, according to some examples;

FIGS. 3B to 3E are diagrams depicting examples of sensor fieldredundancy and autonomous vehicle adaption to a loss of a sensor field,according to some examples;

FIG. 4 is a functional block diagram depicting a system including anautonomous vehicle service platform that is communicatively coupled viaa communication layer to an autonomous vehicle controller, according tosome examples;

FIG. 5 is an example of a flow diagram to control an autonomous vehicle,according to some embodiments;

FIG. 6 is a diagram depicting an example of an architecture for anautonomous vehicle controller, according to some embodiments;

FIG. 7 is a diagram depicting an example of an autonomous vehicleservice platform implementing redundant communication channels tomaintain reliable communications with a fleet of autonomous vehicles,according to some embodiments;

FIG. 8 is a diagram depicting an example of a messaging applicationconfigured to exchange data among various applications, according tosome embodiment;

FIG. 9 is a diagram depicting types of data for facilitatingteleoperations using a communications protocol described in FIG. 8,according to some examples;

FIG. 10 is a diagram illustrating an example of a teleoperator interfacewith which a teleoperator may influence path planning, according to someembodiments;

FIG. 11 is a diagram depicting an example of a planner configured toinvoke teleoperations, according to some examples;

FIG. 12 is an example of a flow diagram configured to control anautonomous vehicle, according to some embodiments;

FIG. 13 depicts an example in which a planner may generate a trajectory,according to some examples;

FIG. 14 is a diagram depicting another example of an autonomous vehicleservice platform, according to some embodiments;

FIG. 15 is an example of a flow diagram to control an autonomousvehicle, according to some embodiments;

FIG. 16 is a diagram of an example of an autonomous vehicle fleetmanager implementing a fleet optimization manager, according to someexamples;

FIG. 17 is an example of a flow diagram for managing a fleet ofautonomous vehicles, according to some embodiments;

FIG. 18 is a diagram illustrating an autonomous vehicle fleet managerimplementing an autonomous vehicle communications link manager,according to some embodiments;

FIG. 19 is an example of a flow diagram to determine actions forautonomous vehicles during an event, according to some embodiments;

FIG. 20 is a diagram depicting an example of a localizer, according tosome embodiments;

FIG. 21 is an example of a flow diagram to generate local pose data 25based on integrated sensor data, according to some embodiments;

FIG. 22 is a diagram depicting another example of a localizer, accordingto some embodiments;

FIG. 23 is a diagram depicting an example of a perception engine,according to some embodiments;

FIG. 24 is an example of a flow chart to generate perception enginedata, according to some embodiments;

FIG. 25 is an example of a segmentation processor, according to someembodiments;

FIG. 26A is a diagram depicting examples of an object tracker and aclassifier, according to various embodiments;

FIG. 26B is a diagram depicting another example of an object trackeraccording to at least some examples;

FIG. 27 is an example of front-end processor for a perception engine,according to some examples;

FIG. 28 is a diagram depicting a simulator configured to simulate anautonomous vehicle in a synthetic environment, according to variousembodiments;

FIG. 29 is an example of a flow chart to simulate various aspects of anautonomous vehicle, according to some embodiments;

FIG. 30 is an example of a flow chart to generate map data, according tosome embodiments;

FIG. 31 is a diagram depicting an architecture of a mapping engine,according to some embodiments

FIG. 32 is a diagram depicting an autonomous vehicle application,according to some examples; and

FIGS. 33 to 35 illustrate examples of various computing platformsconfigured to provide various functionalities to components of anautonomous vehicle service, according to various embodiments;

FIG. 36 is a diagram depicting an example of a teleoperator managerconfigured to at least influence navigational control for an autonomousvehicle, according to some examples;

FIG. 37 is a diagram depicting an example of a teleoperator managerconfigured to implement a user interface to facilitate teleoperationsservice commands, according to some examples;

FIG. 38 is a diagram depicting another example of a teleoperator managerconfigured to implement a user interface to facilitate teleoperationsservice commands, according to some examples;

FIG. 39 is a diagram depicting yet another example of a teleoperatormanager configured to implement a user interface to facilitateteleoperations service commands, according to some examples;

FIG. 40 is a flow chart illustrating an example of performingteleoperation services, according to some examples; and

FIG. 41 illustrates examples of various computing platforms configuredto provide various teleoperations-related functionalities and/orstructures to components of an autonomous vehicle service, according tovarious embodiments.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, an apparatus, a user interface, or aseries of program instructions on a computer readable medium such as acomputer readable storage medium or a computer network where the programinstructions are sent over optical, electronic, or wirelesscommunication links. In general, operations of disclosed processes maybe performed in an arbitrary order, unless otherwise provided in theclaims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims, and numerousalternatives, modifications, and equivalents thereof. Numerous specificdetails are set forth in the following description in order to provide athorough understanding. These details are provided for the purpose ofexample and the described techniques may be practiced according to theclaims without some or all of these specific details. For clarity,technical material that is known in the technical fields related to theexamples has not been described in detail to avoid unnecessarilyobscuring the description.

FIG. 1 is a diagram depicting an implementation of a fleet of autonomousvehicles that are communicatively networked to an autonomous vehicleservice platform, according to some embodiments. Diagram 100 depicts afleet of autonomous vehicles 109 (e.g., one or more of autonomousvehicles 109 a to 10 ge) operating as a service, each autonomous vehicle109 being configured to self-drive a road network 110 and establish acommunication link 192 with an autonomous vehicle service platform 101.In examples in which a fleet of autonomous vehicles 109 constitutes aservice, a user 102 may transmit a request 103 for autonomoustransportation via one or more networks 106 to autonomous vehicleservice platform 101. In response, autonomous vehicle service platform101 may dispatch one of autonomous vehicles 109 to transport user 102autonomously from geographic location 119 to geographic location 111.Autonomous vehicle service platform 101 may dispatch an autonomousvehicle from a station 190 to geographic location 119, or may divert anautonomous vehicle 109 c, already in transit (e.g., without occupants),to service the transportation request for 5 user 102. Autonomous vehicleservice platform 101 may be further configured to divert an autonomousvehicle 109 c in transit, with passengers, responsive to a request fromuser 102 (e.g., as a passenger). In addition, autonomous vehicle serviceplatform 101 may be configured to reserve an autonomous vehicle 109 c intransit, with passengers, for diverting to service a request of user 102subsequent to dropping off existing passengers. Note that multipleautonomous vehicle service platforms 101 (not shown) and one or morestations 190 may be implemented to service one or more autonomousvehicles 109 in connection with road network 110. One or more stations190 may be configured to store, service, manage, and/or maintain aninventory of autonomous vehicles 109 (e.g., station 190 may include oneor more computing devices implementing autonomous 15 vehicle serviceplatform 101).

According to some examples, at least some of autonomous vehicles 109 ato 10 ge are configured as bidirectional autonomous vehicles, such asbidirectional autonomous vehicle (“AV”) 130. Bidirectional autonomousvehicle 130 may be configured to travel in either direction principallyalong, but not limited to, a longitudinal axis 131. Accordingly,bidirectional autonomous vehicle 130 may be configured to implementactive lighting external to the vehicle to alert others (e.g., otherdrivers, pedestrians, cyclists, etc.) in the adjacent vicinity, and adirection in which bidirectional autonomous vehicle 130 is traveling.For example, active sources of light 136 may be implemented as activelights 138 a when traveling in a first direction, or may be implementedas active lights 138 b when traveling in a second direction. Activelights 138 a may be implemented using a first subset of one or morecolors, with optional animation (e.g., light patterns of variableintensities of light or color that may change over time). Similarly,active lights 138 b may be implemented using a second subset of one ormore colors and light patterns that may be different than those ofactive lights 138 a. For example, active lights 138 a may be implementedusing white-colored lights as “headlights,” whereas active lights 138 bmay be implemented using red-colored lights as “taillights.” Activelights 138 a and 138 b, or portions thereof, may be configured toprovide other light-related functionalities, such as provide “turnsignal indication” functions (e.g., using yellow light). According tovarious examples, logic in autonomous vehicle 130 may be configured toadapt active lights 138 a and 138 b to comply with various safetyrequirements and traffic regulations or laws for any number ofjurisdictions.

In some embodiments, bidirectional autonomous vehicle 130 may beconfigured to have similar structural elements and components in eachquad portion, such as quad portion 194. The quad portions are depicted,at least in this example, as portions of bidirectional autonomousvehicle 130 defined by the intersection of a plane 132 and a plane 134,both of which pass through the vehicle to form two similar halves oneach side of planes 132 and 134. Further, bidirectional autonomousvehicle 130 may include an autonomous vehicle controller 147 thatincludes logic (e.g., hardware or software, or as combination thereof)that is configured to control a predominate number of vehicle functions,including driving control (e.g., propulsion, steering, etc.) and activesources 136 of light, among other functions. Bidirectional autonomousvehicle 130 also includes a number of sensors 139 disposed at variouslocations on the vehicle (other sensors are not shown).

Autonomous vehicle controller 147 may be further configured to determinea local pose (e.g., local position) of an autonomous vehicle 109 and todetect external objects relative to the vehicle. For example, considerthat bidirectional autonomous vehicle 130 is traveling in the direction119 in road network 110. A localizer (not shown) of autonomous vehiclecontroller 147 can determine a local pose at the geographic location111. As such, the localizer may use acquired sensor data, such as sensordata associated with surfaces of buildings 115 and 117, which can becompared against reference data, such as map data (e.g., 3D map data,including reflectance data) to determine a local pose. Further, aperception engine (not shown) of autonomous vehicle controller 147 maybe configured to detect, classify, and predict the behavior of externalobjects, such as external object 112 (a “tree”) and external object 114(a “pedestrian”). Classification of such external objects may broadlyclassify objects as static objects, such as external object 112, anddynamic objects, such as external object 114. The localizer and theperception engine, as well as other components of the AV controller 147,collaborate to cause autonomous vehicles 109 to drive autonomously.

According to some examples, autonomous vehicle service platform 101 isconfigured to provide teleoperator services should an autonomous vehicle109 request teleoperation. For example, consider that an autonomousvehicle controller 147 in 10 autonomous vehicle 109 d detects an object126 obscuring a path 124 on roadway 122 at point 191, as depicted ininset 120. If autonomous vehicle controller 147 cannot ascertain a pathor trajectory over which vehicle 109 d may safely transit with arelatively high degree of certainty, then autonomous vehicle controller147 may transmit request message 105 for teleoperation services. Inresponse, a teleoperator computing device 104 15 may receiveinstructions from a teleoperator 108 to perform a course of action tosuccessfully (and safely) negotiate obstacles 126. Response data 107then can be transmitted back to autonomous vehicle 109 d to cause thevehicle to, for example, safely cross a set of double lines as ittransits along the alternate path 121. In some examples, teleoperatorcomputing device 104 may generate a response identifying geographicareas to exclude from planning a path. In particular, rather thanprovide a path to follow, a teleoperator 108 may define areas orlocations that the autonomous vehicle must avoid.

In view of the foregoing, the structures and/or functionalities ofautonomous vehicle 130 and/or autonomous vehicle controller 147, as wellas their components, can perform real-time (or near real-time)trajectory calculations through autonomous-related operations, such aslocalization and perception, to enable autonomous vehicles 109 toself-drive.

In some cases, the bidirectional nature of bidirectional autonomousvehicle 130 provides for a vehicle that has quad portions 194 (or anyother number of symmetric portions) that are similar or aresubstantially similar to each other. Such symmetry reduces complexity ofdesign and decreases relatively the number of unique components orstructures, thereby reducing inventory and manufacturing complexities.For example, a drivetrain and wheel system may be disposed in any of thequad portions 194. Further, autonomous vehicle controller 147 isconfigured to invoke teleoperation services to reduce the likelihoodthat an autonomous vehicle 109 is delayed in transit while resolving anevent or issue that may otherwise affect the safety of the occupants. Insome cases, the visible portion of road network 110 depicts a geo-fencedregion that may limit or otherwise control the movement of autonomousvehicles 109 to the road network shown in FIG. 1. According to variousexamples, autonomous vehicle 109, and a fleet thereof, may beconfigurable to operate as a level 4 (“full self-driving automation,” orL4) vehicle that can provide transportation on demand with theconvenience and privacy of point-to-point personal mobility whileproviding the efficiency of shared vehicles. In some examples,autonomous vehicle 109, or any autonomous vehicle described herein, maybe configured to omit a steering wheel or any other mechanical means ofproviding manual (i.e., human-controlled) steering for autonomousvehicle 109. Further, autonomous vehicle 109, or any autonomous vehicledescribed herein, may be configured to omit a seat or location reservedwithin the vehicle for an occupant to engage a steering wheel or anymechanical interface for a steering system.

FIG. 2 is an example of a flow diagram to monitor a fleet of autonomousvehicles, according to some embodiments. At 202, flow 200 begins when afleet of autonomous vehicles are monitored. At least one autonomousvehicle includes an autonomous vehicle controller configured to causethe vehicle to autonomously transit from a first geographic region to asecond geographic region. At 204, data representing an event associatedwith a calculated confidence level for a vehicle is detected. An eventmay be a condition or situation affecting operation, or potentiallyaffecting operation, of an autonomous vehicle. The events may beinternal to an autonomous vehicle, or external. For example, an obstacleobscuring a roadway may be viewed as an event, as well as a reduction orloss of communication. An event may include traffic conditions orcongestion, as well as unexpected or unusual numbers or types ofexternal objects (or tracks) that are perceived by a perception engine.An event may include weather-related conditions (e.g., loss of frictiondue to ice or rain) or the angle at which the sun is shining (e.g., atsunset), such as low angle to the horizon that cause sun to shinebrightly in the eyes of human drivers of other vehicles. These and otherconditions may be viewed as events that cause invocation of theteleoperator service or for the vehicle to execute a safe-stoptrajectory.

At 206, data representing a subset of candidate trajectories may bereceived from an autonomous vehicle responsive to the detection of theevent. For example, a planner of an autonomous vehicle controller maycalculate and evaluate large numbers of trajectories (e.g., thousands orgreater) per unit time, such as a second. In some embodiments, candidatetrajectories are a subset of the trajectories that provide forrelatively higher confidence levels that an autonomous vehicle may moveforward safely in view of the event (e.g., using an alternate pathprovided by a teleoperator). Note that some candidate trajectories maybe ranked or associated with higher degrees of confidence than othercandidate trajectories. According to some examples, subsets of candidatetrajectories may originate from any number of sources, such as aplanner, a teleoperator computing device (e.g., teleoperators candetermine and provide approximate paths), etc., and may be combined as asuperset of candidate trajectories. At 208, path guidance data may beidentified at one or more processors. The path guidance data may beconfigured to assist a teleoperator in selecting a guided trajectoryfrom one or more of the candidate trajectories. In some instances, thepath guidance data specifies a value indicative of a confidence level orprobability that indicates the degree of certainty that a particularcandidate trajectory may reduce or negate the probability that the eventmay impact operation of an autonomous vehicle. A guided trajectory, as aselected candidate trajectory, may be received at 210, responsive toinput from a teleoperator (e.g., a teleoperator may select at least onecandidate trajectory as a guided trajectory from a group ofdifferently-ranked candidate trajectories). The selection may be madevia an operator interface that lists a number of candidate trajectories,for example, in order from highest confidence levels to lowestconfidence levels. At 212, the selection of a candidate trajectory as aguided trajectory may be transmitted to the vehicle, which, in turn,implements the guided trajectory for resolving the condition by causingthe vehicle to perform a teleoperator-specified maneuver. As such, theautonomous vehicle may transition from a non-normative operationalstate.

FIG. 3A is a diagram depicting examples of sensors and other autonomousvehicle components, according to some examples. Diagram 300 depicts aninterior view of a bidirectional autonomous vehicle 330 that includessensors, signal routers 345, drive trains 349, removable batteries 343,audio generators 344 (e.g., speakers or transducers), and autonomousvehicle (“AV”) control logic 347. Sensors shown in diagram 300 includeimage capture sensors 340 (e.g., light capture devices or cameras of anytype), audio capture sensors 342 (e.g., microphones of any type), radardevices 348, sonar devices 341 (or other like sensors, includingultrasonic sensors or acoustic-related sensors), and Lidar devices 346,among other sensor types and modalities (some of which are not shown,such inertial measurement units, or “IMUs,” global positioning system(“GPS”) sensors, sonar sensors, etc.). Note that quad portion 350 isrepresentative of the symmetry of each of four “quad portions” ofbidirectional autonomous vehicle 330 (e.g., each quad portion 350 mayinclude a wheel, a drivetrain 349, similar steering mechanisms, similarstructural support and members, etc. beyond that which is depicted). Asdepicted in FIG. 3A, similar sensors may be placed in similar locationsin each quad portion 350, however any other configuration mayimplemented. Each wheel may be steerable individually and independent ofthe others. Note, too, that removable batteries 343 may be configured tofacilitate being swapped in and swapped out rather than charging insitu, thereby ensuring reduced or negligible downtimes due to thenecessity of charging batteries 343. While autonomous vehicle controller347 a is depicted as being used in a bidirectional autonomous vehicle330, autonomous vehicle controller 347 a is not so limited and may beimplemented in unidirectional autonomous vehicles or any other type ofvehicle, whether on land, in air, or at sea. Note that the depicted anddescribed positions, locations, orientations, quantities, and types ofsensors shown in FIG. 3A are not intended to be limiting, and, as such,there may be any number and type of sensor, and any sensor may belocated and oriented anywhere on autonomous vehicle 330.

According to some embodiments, portions of the autonomous vehicle (“AV”)control logic 347 may be implemented using clusters of graphicsprocessing units (“GPUs”) implementing a framework and programming modelsuitable for programming the clusters of GPUs. For example, a computeunified device architecture (“CUDA”) compatible programming language andapplication programming interface (“API”) model may be used to programthe GPUs. CUDA™ is produced and maintained by NVIDIA of Santa Clara,Calif. Note that other programming languages may be implemented, 10 suchas OpenCL, or any other parallel programming language.

According to some embodiments, autonomous vehicle control logic 347 maybe implemented in hardware and/or software as autonomous vehiclecontroller 347 a, which is shown to include a motion controller 362, aplanner 364, a perception engine 366, and a localizer 368. As shown,autonomous vehicle controller 347 a is configured to 15 receive cameradata 340 a, Lidar data 346 a, and radar data 348 a, or any otherrange-sensing or localization data, including sonar data 341 a or thelike. Autonomous vehicle controller 347 a is also configured to receivepositioning data, such as GPS data 352, IMU data 354, and otherposition-sensing data (e.g., wheel-related data, such as steeringangles, angular velocity, etc.). Further, autonomous vehicle controller347 a may receive any other sensor data 356, as well as reference data339. In some cases, reference data 339 includes map data (e.g., 3D mapdata, 2D map data, 4D map data (e.g., including Epoch Determination))and route data (e.g., road network data, including, but not limited to,RNDF data (or similar data), MDF data (or similar data), etc.

Localizer 368 is configured to receive sensor data from one or more 25sources, such as GPS data 352, wheel data, IMU data 354, Lidar data 346a, camera data 340 a, radar data 348 a, and the like, as well asreference data 339 (e.g., 3D map data and route data). Localizer 368integrates (e.g., fuses the sensor data) and analyzes the data bycomparing sensor data to map data to determine a local pose (orposition) of bidirectional autonomous vehicle 330. According to someexamples, localizer 368 may generate or update the pose or position ofany autonomous vehicle in real-time or near real-time. Note thatlocalizer 368 and its functionality need not be limited to“bi-directional” vehicles and can be implemented in any vehicle of anytype. Therefore, localizer 368 (as well as other components of AVcontroller 347 a) may be implemented in a “uni-directional” vehicle orany non-autonomous vehicle. According to some embodiments, datadescribing a local pose may include one or more of an x-coordinate, ay-coordinate, a z-coordinate (or any coordinate of any coordinatesystem, including polar or cylindrical coordinate systems, or the like),a yaw value, a roll value, a pitch value (e.g., an angle value), a rate(e.g., velocity), altitude, and the like.

Perception engine 366 is configured to receive sensor data from one ormore sources, such as Lidar data 346 a, camera data 340 a, radar data348 a, and the like, as well as local pose data. Perception engine 366may be configured to determine locations of external objects based onsensor data and other data. External objects, for instance, may beobjects that are not part of a drivable surface. For example, perceptionengine 366 may be able to detect and classify external objects aspedestrians, bicyclists, dogs, other vehicles, etc. (e.g., perceptionengine 366 is configured to classify the objects in accordance with atype of classification, which may be associated with semanticinformation, including a label). Based on the classification of theseexternal objects, the external objects may be labeled as dynamic objectsor static objects. For example, an external object classified as a treemay be labeled as a static object, while an external object classifiedas a pedestrian may be labeled as a static object. External objectslabeled as static mayor may not be described in map data. Examples ofexternal objects likely to be labeled as static include traffic cones,cement barriers arranged across a roadway, lane closure signs,newly-placed mailboxes or trash cans adjacent a roadway, etc. Examplesof external objects likely to be labeled as dynamic include bicyclists,pedestrians, animals, other vehicles, etc. If the external object islabeled as dynamic, and further data about the external object mayindicate a typical level of activity and velocity, as well as behaviorpatterns associated with the classification type. Further data about theexternal object may be generated by tracking the external object. Assuch, the classification type can be used to predict or otherwisedetermine the likelihood that an external object may, for example,interfere with an autonomous vehicle traveling along a planned path. Forexample, an external object that is classified as a pedestrian may beassociated with some maximum speed, as well as an average speed (e.g.,based on tracking data). The velocity of the pedestrian relative to thevelocity of an autonomous vehicle can be used to determine if acollision is likely. Further, perception engine 364 may determine alevel of uncertainty associated with a current and future state ofobjects. In some examples, the level of uncertainty may be expressed asan estimated value (or probability).

Planner 364 is configured to receive perception data from perceptionengine 366, and may also include localizer data from localizer 368.According to some examples, the perception data may include an obstaclemap specifying static and dynamic objects located in the vicinity of anautonomous vehicle, whereas the localizer data may include a local poseor position. In operation, planner 364 generates numerous trajectories,and evaluates the trajectories, based on at least the location of theautonomous vehicle against relative locations of external dynamic andstatic objects. Planner 364 selects an optimal trajectory based on avariety of criteria over which to direct the autonomous vehicle in waythat provides for collision-free travel. In some examples, planner 364may be configured to calculate the trajectories asprobabilistically-determined trajectories. Further, planner 364 maytransmit steering and propulsion commands (as well as decelerating orbraking commands) to motion controller 362. Motion controller 362subsequently may convert any of the commands, such as a steeringcommand, a throttle or propulsion command, and a braking command, intocontrol signals (e.g., for application to actuators or other mechanicalinterfaces) to implement changes in steering or wheel angles 351 and/orvelocity 353.

FIGS. 3B to 3E are diagrams depicting examples of sensor fieldredundancy and autonomous vehicle adaption to a loss of a sensor field,according to some examples. Diagram 391 of FIG. 3B depicts a sensorfield 301 a in which sensor 310 a detects objects (e.g., for determiningrange or distance, or other information). While sensor 310 a mayimplement any type of sensor or sensor modality, sensor 310 a andsimilarly-described sensors, such as sensors 310 b, 310 c, and 310 d,may include Lidar devices. Therefore, sensor fields 301 a, 301 b, 301 c,and 301 d each includes a field into which lasers extend. Diagram 392 ofFIG. 3C depicts four overlapping sensor fields each of which isgenerated by a corresponding Lidar sensor 310 (not shown). As shown,portions 301 of the sensor fields include no overlapping sensor fields(e.g., a single Lidar field), portions 302 of the sensor fields includetwo overlapping sensor fields, and portions 303 include threeoverlapping sensor fields, whereby such sensors provide for multiplelevels of redundancy should a Lidar sensor fail.

FIG. 3D depicts a loss of a sensor field due to failed operation ofLidar 309, according to some examples. Sensor field 302 of FIG. 3C istransformed into a single sensor field 305, one of sensor fields 301 ofFIG. 3C is lost to a gap 304, and three of sensor fields 303 of FIG. 3Care transformed into sensor fields 306 (i.e., limited to two overlappingfields). Should autonomous car 330 c be traveling in the direction oftravel 15 396, the sensor field in front of the moving autonomousvehicle may be less robust than the one at the trailing end portion.According to some examples, an autonomous vehicle controller (not shown)is configured to leverage the bidirectional nature of autonomous vehicle330 c to address the loss of sensor field at the leading area in frontof the vehicle. FIG. 3E depicts a bidirectional maneuver for restoring acertain robustness of the sensor field in front of autonomous vehicle330 d. As shown, a more robust sensor field 302 is disposed at the rearof the vehicle 330 d coextensive with taillights 348. When convenient,autonomous vehicle 330 d performs a bidirectional maneuver by pullinginto a driveway 397 and switches its directionality such that taillights348 actively switch to the other side (e.g., the trailing edge) ofautonomous vehicle 330 d. As shown, autonomous vehicle 330 d restores arobust sensor field 302 in front of the vehicle as it travels alongdirection of travel 398. Further, the above-described bidirectionalmaneuver obviates a requirement for a more complicated maneuver thatrequires backing up into a busy roadway.

FIG. 4 is a functional block diagram depicting a system including anautonomous vehicle service platform that is communicatively coupled viaa communication layer to an autonomous vehicle controller, according tosome examples. Diagram 400 depicts an autonomous vehicle controller(“AV”) 447 disposed in an autonomous vehicle 430, which, in turn,includes a number of sensors 470 coupled to autonomous vehiclecontroller 447. Sensors 470 include one or more Lidar devices 472, oneor more cameras 474, one or more radars 476, one or more globalpositioning system (“GPS”) data receiver-sensors, one or more inertialmeasurement units (“IMUs”) 475, one or more odometry sensors 477 (e.g.,wheel encoder sensors, wheel speed sensors, and the like), and any othersuitable sensors 478, such as infrared cameras or sensors,hyperspectral-capable sensors, ultrasonic sensors (or any other acousticenergy-based sensor), radio frequency-based sensors, etc. In some cases,wheel angle sensors configured to sense steering angles of wheels may beincluded as odometry sensors 477 or suitable sensors 478. In anon-limiting example, autonomous vehicle controller 447 may include fouror more Lidars 472, sixteen or more cameras 474 and four or more radarunits 476. Further, sensors 470 may be configured to provide sensor datato components of autonomous vehicle controller 447 and to elements ofautonomous vehicle service platform 401. As shown in diagram 400,autonomous vehicle controller 447 includes a planner 464, a motioncontroller 462, a localizer 468, a perception engine 466, and a localmap generator 440. Note that elements depicted in diagram 400 of FIG. 4may include structures and/or functions as similarly-named elementsdescribed in connection to one or more other drawings.

Localizer 468 is configured to localize autonomous vehicle (i.e.,determine a local pose) relative to reference data, which may includemap data, route data (e.g., road network data, such as RNDF-like data),and the like. In some cases, localizer 468 is configured to identity,for example, a point in space that may represent a location ofautonomous vehicle 430 relative to features of a representation of anenvironment. Localizer 468 is shown to include a sensor data integrator469, which may be configured to integrate multiple subsets of sensordata (e.g., of different sensor modalities) to reduce uncertaintiesrelated to each individual type of sensor. According to some examples,sensor data integrator 469 is configured to fuse sensor data (e.g.,Lidar data, camera data, radar data, etc.) to form integrated sensordata values for determining a local pose. According to some examples,localizer 468 retrieves reference data originating from a reference datarepository 405, which includes a map data repository 405 a for storing2D map data, 3D map data, 4D map data, and the like. Localizer 468 maybe configured to identify at least a subset of features in theenvironment to match against map data to identify, or otherwise confirm,a pose of autonomous vehicle 430. According to some examples, localizer468 may be configured to identify any amount of features in an 10environment, such that a set of features can one or more features, orall features. In a specific example, any amount of Lidar data (e.g.,most or substantially all Lidar data) may be compared against datarepresenting a map for purposes of localization. Generally, non-matchedobjects resulting from the comparison of the environment features andmap data may be a dynamic object, such as a vehicle, bicyclist,pedestrian, etc. Note that detection of dynamic objects, includingobstacles, may be performed with or without map data. In particular,dynamic objects may be detected and tracked independently of map data(i.e., in the absence of map data). In some instances, 2D map data and3D map data may be viewed as “global map data” or map data that has beenvalidated at a point in time by autonomous vehicle service platform 401.As map data in map data repository 405 a may be updated and/or validatedperiodically, a deviation may exist between the map data and an actualenvironment in which the autonomous vehicle is positioned. Therefore,localizer 468 may retrieve locally-derived map data generated by localmap generator 440 to enhance localization. Local map generator 440 isconfigured to generate local map data in real-time or near real-time.Optionally, local 25 map generator 440 may receive static and dynamicobject map data to enhance the accuracy of locally generated maps by,for example, disregarding dynamic objects in localization. According toat least some embodiments, local map generator 440 may be integratedwith, or formed as part of, localizer 468. In at least one case, localmap generator 440, either individually or in collaboration withlocalizer 468, may be configured to generate map and/or reference databased on simultaneous localization and mapping (“SLAM”) or the like.Note that localizer 468 may implement a “hybrid” approach to using mapdata, whereby logic in localizer 468 may be configured to select variousamounts of map data from either map data repository 405 a or local mapdata from local map generator 440, depending on the degrees ofreliability of each source of map data. Therefore, localizer 468 maystill use out-of-date map data in view of locally-generated map data.

Perception engine 466 is configured to, for example, assist planner 464in planning routes and generating trajectories by identifying objects ofinterest in a surrounding environment in which autonomous vehicle 430 istransiting. Further, probabilities may be associated with each of theobject of interest, whereby a probability may represent a likelihoodthat an object of interest may be a threat to safe travel (e.g., afast-moving motorcycle may require enhanced tracking rather than aperson sitting at a bus stop bench while reading a newspaper). As shown,perception engine 466 includes an object detector 442 and an objectclassifier 444. Object detector 442 is configured to distinguish objectsrelative to other features in the environment, and object classifier 444may be configured to classify objects as either dynamic or staticobjects and track the locations of the dynamic and the static objectsrelative to autonomous vehicle 430 for planning purposes. Further,perception engine 466 may be configured to assign an identifier to astatic or dynamic object that specifies whether the object is (or hasthe potential to become) an obstacle that may impact path planning atplanner 464. Although not shown in FIG. 4, note that perception engine466 may also perform other perception-related functions, such assegmentation and tracking, examples of which are described below.

Planner 464 is configured to generate a number of candidate trajectoriesfor accomplishing a goal to reaching a destination via a number of pathsor routes that are available. Trajectory evaluator 465 is configured toevaluate candidate trajectories and identify which subsets of candidatetrajectories are associated with higher degrees of confidence levels ofproviding collision-free paths to the destination. As such, trajectoryevaluator 465 can select an optimal trajectory based on relevantcriteria for causing commands to generate control signals for vehiclecomponents 450 (e.g., actuators or other mechanisms). Note that therelevant criteria may include any number of factors that define optimaltrajectories, the selection of which need not be limited to reducingcollisions. For example, the selection of trajectories may be made tooptimize user experience (e.g., user comfort) as well as collision-freetrajectories that comply with traffic regulations and laws. Userexperience may be optimized by moderating accelerations in variouslinear and angular directions (e.g., to reduce jerking-like travel orother unpleasant motion). In some cases, at least a portion of therelevant criteria can specify which of the other criteria to override orsupersede, while maintain optimized, collision-free travel. For example,legal restrictions may be temporarily lifted or deemphasized whengenerating trajectories in limited situations (e.g., crossing doubleyellow lines to go around a cyclist or travelling at higher speeds thanthe posted speed limit to match traffic flows). As such, the controlsignals are configured to cause propulsion and directional changes atthe drivetrain and/or wheels. In this example, motion controller 462 isconfigured to transform commands into control signals (e.g., velocity,wheel angles, etc.) for controlling the mobility of autonomous vehicle430. In the event that trajectory evaluator 465 has insufficientinformation to ensure a confidence level high enough to providecollision-free, optimized travel, planner 464 can generate a request toteleoperator 404 for teleoperator support.

Autonomous vehicle service platform 401 includes teleoperator 404 (e.g.,a teleoperator computing device), reference data repository 405, a mapupdater 406, a vehicle data controller 408, a calibrator 409, and anoff-line object classifier 410. Note that each element of autonomousvehicle service platform 401 may be independently located or distributedand in communication with other elements in autonomous vehicle serviceplatform 401. Further, element of autonomous vehicle service platform401 may independently communicate with the autonomous vehicle 430 viathe communication layer 402. Map updater 406 is configured to receivemap data (e.g., from local map generator 440, sensors 460, or any othercomponent of autonomous vehicle controller 447), and is furtherconfigured to detect deviations, for example, of map data in map datarepository 405 a from a locally-generated map. Vehicle data controller408 can cause map updater 406 to update reference data within repository405 and facilitate updates to 2D, 3D, and/or 4D map data. In some cases,vehicle data controller 408 can control the rate at which local map datais received into autonomous vehicle service platform 408 as well as thefrequency at which map updater 406 performs updating of the map data.

Calibrator 409 is configured to perform calibration of various sensorsof the same or different types. Calibrator 409 may be configured todetermine the relative poses of the sensors (e.g., in Cartesian space(x, y, z)) and orientations of the sensors (e.g., roll, pitch and yaw).The pose and orientation of a sensor, such a camera, Lidar sensor, radarsensor, etc., may be calibrated relative to other sensors, as well asglobally relative to the vehicle's reference frame. Off-lineself-calibration can also calibrate or estimate other parameters, suchas vehicle inertial tensor, wheel base, wheel radius or surface roadfriction. Calibration can also be done online to detect parameterchange, according to some examples. Note, too, that calibration bycalibrator 409 may include intrinsic parameters of the sensors (e.g.,optical distortion, beam angles, etc.) and extrinsic parameters. In somecases, calibrator 409 may be performed by maximizing a correlationbetween depth discontinuities in 3D laser data and edges of image data,as an example. Off-line object classification 410 is configured toreceive data, such as sensor data, from sensors 470 or any othercomponent of autonomous vehicle controller 447. According to someembodiments, an off-line classification pipeline of off-line objectclassification 410 may be configured to pre-collect and annotate objects(e.g., manually by a human and/or automatically using an offlinelabeling algorithm), and may further be configured to train an onlineclassifier (e.g., object classifier 444), which can provide 25 real-timeclassification of object types during online autonomous operation.

FIG. 5 is an example of a flow diagram to control an autonomous vehicle,according to some embodiments. At 502, flow 500 begins when sensor dataoriginating from sensors of multiple modalities at an autonomous vehicleis received, for example, by an autonomous vehicle controller. One ormore subsets of sensor data may be integrated for generating fused datato improve, for example, estimates. In some examples, a sensor stream ofone or more sensors (e.g., of same or different modalities) may be fusedto form fused sensor data at 504. In some examples, subsets of Lidarsensor data and camera sensor data may be fused at 504 to facilitatelocalization. At 506, data representing objects based on the least twosubsets of sensor data may be derived at a processor. For example, dataidentifying static objects or dynamic objects may be derived (e.g., at aperception engine) from at least Lidar and camera data. At 508, adetected object is determined to affect a planned path, and a subset oftrajectories are evaluated (e.g., at a planner) responsive to thedetected object at 510. A confidence level is determined at 512 toexceed a range of acceptable confidence levels associated with normativeoperation of an autonomous vehicle. Therefore, in this case, aconfidence level may be such that a certainty of selecting an optimizedpath is less likely, whereby an optimized path may be determined as afunction of the probability of facilitating collision-free travel,complying with traffic laws, providing a comfortable user experience(e.g., comfortable ride), and/or generating candidate trajectories onany other factor. As such, a request for an alternate path may betransmitted to a teleoperator computing device at 514. Thereafter, theteleoperator computing device may provide a planner with an optimaltrajectory over which an autonomous vehicle made travel. In situations,the vehicle may also determine that executing a safe-stop maneuver isthe best course of action (e.g., safely and automatically causing anautonomous vehicle to a stop at a location of relatively lowprobabilities of danger). Note that the order depicted in this and otherflow charts herein are not intended to imply a requirement to linearlyperform various functions as each portion of a flow chart may beperformed serially or in parallel with anyone or more other portions ofthe flow chart, as well as independent or 25 dependent on other portionsof the flow chart.

FIG. 6 is a diagram depicting an example of an architecture for anautonomous vehicle controller, according to some embodiments. Diagram600 depicts a number of processes including a motion controller process662, a planner processor 664, a perception process 666, a mappingprocess 640, and a localization process 668, some of which may generateor receive data relative to other processes. Other processes, such assuch as processes 670 and 650 may facilitate interactions with one ormore mechanical components of an autonomous vehicle. For example,perception process 666, mapping process 640, and localization process668 are configured to receive sensor data from sensors 670, whereasplanner process 664 and perception process 666 are configured to receiveguidance data 606, which may include route data, such as road networkdata. Further to diagram 600, localization process 668 is configured toreceive map data 605 a (i.e., 2D map data), map data 605 b (i.e., 3D mapdata), and local map data 642, among other types of map data. Forexample, localization process 668 may also receive other forms of mapdata, such as 4D map data, which may include, for example, an epochdetermination. Localization process 668 is configured to generate localposition data 641 representing a local pose. Local position data 641 isprovided to motion controller process 662, planner process 664, andperception process 666. Perception process 666 is configured to generatestatic and dynamic object map data 667, which, in turn, may betransmitted to planner process 664. In some examples, static and dynamicobject map data 667 may be transmitted with other data, such as semanticclassification information and predicted object behavior. Plannerprocess 664 is configured to generate trajectories data 665, whichdescribes a number of trajectories generated by planner 664. Motioncontroller process uses trajectories data 665 to generate low-levelcommands or control signals for application to actuators 650 to causechanges in steering angles and/or velocity.

FIG. 7 is a diagram depicting an example of an autonomous vehicleservice platform implementing redundant communication channels tomaintain reliable communications with a fleet of autonomous vehicles,according to some embodiments. Diagram 700 depicts an autonomous vehicleservice platform 701 including a reference data generator 705, a vehicledata controller 702, an autonomous vehicle fleet manager 703, ateleoperator manager 707, a simulator 740, and a policy manager 742.Reference data generator 705 is configured to generate and modify mapdata and route data (e.g., RNDF data). Further, reference data generator705 may be configured to access 2D maps in 2D map data repository 720,access 3D maps in 3D map data repository 722, and access route data inroute data repository 724. Other map representation data andrepositories may be implemented in some examples, such as 4D map dataincluding Epoch Determination. Vehicle data controller 702 may beconfigured to perform a variety of operations. For example, vehicle datacontroller 702 may be configured to change a rate that data is exchangedbetween a fleet of autonomous vehicles and platform 701 based on qualitylevels of communication over channels 770. During bandwidth-constrainedperiods, for example, data communications may be prioritized such thatteleoperation requests from autonomous vehicle 730 are prioritizedhighly to ensure delivery. Further, variable levels of data abstractionmay be transmitted per vehicle over channels 770, depending on bandwidthavailable for a particular channel. For example, in the presence of arobust network connection, full Lidar data (e.g., substantially allLidar data, but also may be less) may be transmitted, whereas in thepresence of a degraded or low-speed connection, simpler or more abstractdepictions of the data may be transmitted (e.g., bounding boxes withassociated metadata, etc.). Autonomous vehicle fleet manager 703 isconfigured to coordinate the dispatching of autonomous vehicles 730 tooptimize multiple variables, including an efficient use of batterypower, times of travel, whether or not an air-conditioning unit in anautonomous vehicle 730 may be used during low charge states of abattery, etc., any or all of which may be monitored in view ofoptimizing cost functions associated with operating an autonomousvehicle service. An algorithm may be implemented to analyze a variety ofvariables with which to minimize costs or times of travel for a fleet ofautonomous vehicles. Further, autonomous vehicle fleet manager 703maintains an inventory of autonomous vehicles as well as parts foraccommodating a service schedule in view of maximizing up-time of thefleet.

Teleoperator manager 707 IS configured to manage a number ofteleoperator computing devices 704 with which teleoperators 708 provideinput. Simulator 740 is configured to simulate operation of one or moreautonomous vehicles 730, as well as the interactions betweenteleoperator manager 707 and an autonomous vehicle 730. Simulator 740may also simulate operation of a number of sensors (including theintroduction of simulated noise) disposed in autonomous vehicle 730.Further, an environment, such as a city, may be simulated such that asimulated autonomous vehicle can be introduced to the syntheticenvironment, whereby simulated sensors may receive simulated sensordata, such as simulated laser returns. Simulator 740 may provide otherfunctions as well, including validating software updates and/or mapdata. Policy manager 742 is configured to maintain data representingpolicies or rules by which an autonomous vehicle ought to behave in viewof a variety of conditions or events that an autonomous vehicleencounters while traveling in a network of roadways. In some cases,updated policies and/or rules may be simulated in simulator 740 toconfirm safe operation of a fleet of autonomous vehicles in view ofchanges to a policy. Some of the above-described elements of autonomousvehicle service platform 701 are further described hereinafter.

Communication channels 770 are configured to provide networkedcommunication links among a fleet of autonomous vehicles 730 andautonomous vehicle service platform 701. For example, communicationchannel 770 includes a number of different types of networks 771, 772,773, and 774, with corresponding subnetworks (e.g., 771 a to 771 n), toensure a certain level of redundancy for operating an autonomous vehicleservice reliably. For example, the different types of networks incommunication channels 770 may include different cellular networkproviders, different types of data networks, etc., to ensure sufficientbandwidth in the event of reduced or lost communications due to outagesin one or more networks 771, 772, 773, and 774.

FIG. 8 is a diagram depicting an example of a messaging applicationconfigured to exchange data among various applications, according tosome embodiments. Diagram 800 depicts an teleoperator application 801disposed in a teleoperator manager, and an autonomous vehicleapplication 830 disposed in an autonomous vehicle, whereby teleoperatorapplications 801 and autonomous vehicle application 830 exchange messagedata via a protocol that facilitates communications over a variety ofnetworks, such as network 871, 872, and other networks 873. According tosome examples, the communication protocol is a middleware protocolimplemented as a Data Distribution Service™ having a specificationmaintained by the Object Management Group consortium. In accordance withthe communications protocol, teleoperator application 801 and autonomousvehicle application 830 may include a message router 854 disposed in amessage domain, the message router being configured to interface withthe teleoperator API 852. In some examples, message router 854 is arouting service. In some examples, message domain 850 a in teleoperatorapplication 801 may be identified by a teleoperator identifier, whereasmessage domain 850 b be may be identified as a domain associated with avehicle identifier. Teleoperator API 852 in teleoperator application 801is configured to interface with teleoperator processes 803 a to 803 c,whereby teleoperator process 803 b is associated with an autonomousvehicle identifier 804, and teleoperator process 803 c is associatedwith an event identifier 806 (e.g., an identifier that specifies anintersection that may be problematic for collision-free path planning).Teleoperator API 852 in autonomous vehicle application 830 is configuredto interface with an autonomous vehicle operating system 840, whichincludes sensing application 842, a perception application 844, alocalization application 846, and a control application 848. In view ofthe foregoing, the above-described communications protocol mayfacilitate data exchanges to facilitate teleoperations as describedherein. Further, the above-described communications protocol may beadapted to provide secure data exchanges among one or more autonomousvehicles and one or more autonomous vehicle service platforms. Forexample, message routers 854 may be configured to encrypt and decryptmessages to provide for secured interactions between, for example, ateleoperator process 803 and an autonomous vehicle operation system 840.

FIG. 9 is a diagram depicting types of data for facilitatingteleoperations using a communications protocol described in FIG. 8,according to some examples. Diagram 900 depicts a teleoperator 908interfacing with a teleoperator computing device 904 coupled to ateleoperator application 901, which is configured to exchange data via adata-centric messaging bus 972 implemented in one or more networks 971.Data-centric messaging bus 972 provides a communication link betweenteleoperator application 901 and autonomous vehicle application 930.Teleoperator API 962 of teleoperator application 901 is configured toreceive message service configuration data 964 and route data 960, suchas road network data (e.g., RNDF-like data), mission data (e.g.,MDF-data), and the like. Similarly, a messaging service bridge 932 isalso configured to receive messaging service configuration data 934.Messaging service configuration data 934 and 964 provide configurationdata to configure the messaging service between teleoperator application901 and autonomous vehicle application 930. An example of messagingservice configuration data 934 and 964 includes quality of service(“QoS”) configuration data implemented to configure a Data DistributionService™ application.

An example of a data exchange for facilitating teleoperations via thecommunications protocol is described as follows. Consider that obstacledata 920 is generated by a perception system of an autonomous vehiclecontroller. Further, planner options data 924 is generated by a plannerto notify a teleoperator of a subset of candidate trajectories, andposition data 926 is generated by the localizer. Obstacle data 920,planner options data 924, and position data 926 are transmitted to amessaging service bridge 932, which, in accordance with message serviceconfiguration data 934, generates telemetry data 940 and query data 942,both of which are transmitted via data-centric messaging bus 972 intoteleoperator application 901 as telemetry data 950 and query data 952.Teleoperator API 962 receives telemetry data 950 and inquiry data 952,which, in turn are processed in view of Route data 960 and messageservice configuration data 964. The resultant data is subsequentlypresented to a teleoperator 908 via teleoperator computing device 904and/or a collaborative display (e.g., a dashboard display visible to agroup of collaborating teleoperators 908). Teleoperator 908 reviews thecandidate trajectory options that are presented on the display ofteleoperator computing device 904, and selects a guided trajectory,which generates command data 982 and query response data 980, both ofwhich are passed through teleoperator API 962 as query response data 954and command data 956. In turn, query response data 954 and command data956 are transmitted via data-centric messaging bus 972 into autonomousvehicle application 930 as query response data 944 and command data 946.Messaging service bridge 932 receives query response data 944 andcommand data 946 and generates teleoperator command data 928, which isconfigured to generate a teleoperator-selected trajectory forimplementation by a planner. Note that the above-described messagingprocesses are not intended to be limiting, and other messaging protocolsmay be implemented as well.

FIG. 10 is a diagram illustrating an example of a teleoperator interfacewith which a teleoperator may influence path planning, according to someembodiments. Diagram 1000 depicts examples of an autonomous vehicle 1030in communication with an autonomous vehicle service platform 1001, whichincludes a teleoperator manager 10 1007 configured to facilitateteleoperations. In a first example, teleoperator manager 1007 receivesdata that requires teleoperator 1008 to preemptively view a path of anautonomous vehicle approaching a potential obstacle or an area of lowplanner confidence levels so that teleoperator 1008 may be able toaddress an issue in advance. To illustrate, consider that anintersection that an autonomous vehicle is approaching may be tagged asbeing problematic. As such, user interface 1010 displays arepresentation 1014 of a corresponding autonomous vehicle 1030transiting along a path 1012, which has been predicted by a number oftrajectories generated by a planner. Also displayed are other vehicles1011 and dynamic objects 1013, such as pedestrians, that may causesufficient confusion at the planner, thereby requiring teleoperationsupport. User interface 1010 also presents to teleoperator 1008 acurrent velocity 1022, a speed limit 1024, and an amount of charge 1026presently in the batteries. According to some examples, user interface1010 may display other data, such as sensor data as acquired fromautonomous vehicle 1030. In a second example, consider that planner 1064has generated a number of trajectories that are coextensive with aplanner-generated path 1044 regardless of a detected unidentified object1046. Planner 1064 may also generate a subset of candidate trajectories1040, but in this example, the planner is unable to proceed givenpresent confidence levels. If planner 1064 fails to determine analternative path, a teleoperation request may be transmitted. In thiscase, a teleoperator may select one of candidate trajectories 1040 tofacilitate travel by autonomous vehicle 1030 that is consistent withteleoperator-based path 1042.

FIG. 11 is a diagram depicting an example of a planner configured toinvoke teleoperations, according to some examples. Diagram 1100 depictsa planner 1164 including a topography manager 1110, a route manager1112, a path generator 1114, a trajectory evaluator 1120, and atrajectory tracker 1128. Topography manager 1110 is configured toreceive map data, such as 3D map data or other like map data thatspecifies topographic features. Topography manager 1110 is furtherconfigured to identify candidate paths based on topographic-relatedfeatures on a path to a destination. According to various examples,topography manager 1110 receives 3D maps generated by sensors associatedwith one or more autonomous vehicles in the fleet. Route manager 1112 isconfigured to receive environmental data 1103, which may includetraffic-related information associated with one or more routes that maybe selected as a path to the destination. Path generator 1114 receivesdata from topography manager 1110 and route manager 1112, and generatesone or more paths or path segments suitable to direct autonomous vehicletoward a destination. Data representing one or more paths or pathsegments is transmitted into trajectory evaluator 1120.

Trajectory evaluator 1120 includes a state and event manager 1122,which, in turn, may include a confidence level generator 1123.Trajectory evaluator 1120 further includes a guided trajectory generator1126 and a trajectory generator 1124. Further, planner 1164 isconfigured to receive policy data 1130, perception engine data 1132, andlocalizer data 1134.

Policy data 1130 may include criteria with which planner 1164 uses todetermine a path that has a sufficient confidence level with which togenerate trajectories, according to some examples. Examples of policydata 1130 include policies that specify that trajectory generation isbounded by stand-off distances to external objects (e.g., maintaining asafety buffer of 3 feet from a cyclist, as possible), or policies thatrequire that trajectories must not cross a center double yellow line, orpolicies that require trajectories to be limited to a single lane in a4-lane roadway (e.g., based on past events, such as typicallycongregating at a lane closest to a bus stop), and any other similarcriteria specified by policies. Perception engine data 1132 includesmaps of locations of static objects and dynamic objects of interest, andlocalizer data 1134 includes at least a local pose or position.

State and event manager 1122 may be configured to probabilisticallydetermine a state of operation for an autonomous vehicle. For example, afirst state of operation (i.e., “normative operation”) may describe asituation in which trajectories are collision-free, whereas a secondstate of operation (i.e., “non-normative operation”) may describeanother situation in which the confidence level associated with possibletrajectories are insufficient to guarantee collision-free travel.According to some examples, state and event manager 1122 is configuredto use perception data 1132 to determine a state of autonomous vehiclethat is either normative or non-normative. Confidence level generator1123 may be configured to analyze perception data 1132 to determine astate for the autonomous vehicle. For example, confidence levelgenerator 1123 may use semantic information associated with static anddynamic objects, as well as associated probabilistic estimations, toenhance a degree of certainty that planner 1164 is determining safecourse of action. For example, planner 1164 may use perception enginedata 1132 that specifies a probability that an object is either a personor not a person to determine whether planner 1164 is operating safely(e.g., planner 1164 may receive a degree of certainty that an object hasa 98% probability of being a person, and a probability of 2% that theobject is not a person).

Upon determining a confidence level (e.g., based on statistics andprobabilistic determinations) is below a threshold required forpredicted safe operation, a relatively low confidence level (e.g.,single probability score) may trigger planner 1164 to transmit a request1135 for teleoperation support to autonomous vehicle service platform1101. In some cases, telemetry data and a set of candidate trajectoriesmay accompany the request. Examples of telemetry data include sensordata, localization data, perception data, and the like. A teleoperator1108 may transmit via teleoperator computing device 1104 a selectedtrajectory 1137 to guided trajectory generator 1126. As such, selectedtrajectory 1137 is a trajectory formed with guidance from ateleoperator. Upon confirming there is no change in the state (e.g., anon-normative state is pending), guided trajectory generator 1126 passesdata to trajectory generator 1124, which, in turn, causes trajectorytracker 1128, as a trajectory tracking controller, to use theteleop-specified trajectory for generating control signals 1170 (e.g.,steering angles, velocity, etc.). Note that planner 1164 may triggertransmission of a request 1135 for teleoperation support prior to astate transitioning to a non-normative state. In particular, anautonomous vehicle controller and/or its components can predict that adistant obstacle may be problematic and preemptively cause planner 1164to invoke teleoperations prior to the autonomous vehicle reaching theobstacle. Otherwise, the autonomous vehicle may cause a delay bytransitioning to a safe state upon encountering the obstacle or scenario(e.g., pulling over and off the roadway). In another example,teleoperations may be automatically invoked prior to an autonomousvehicle approaching a particular location that is known to be difficultto navigate. This determination may optionally take into considerationother factors, including the time of day, the position of the sun, ifsuch situation is likely to cause a disturbance to the reliability ofsensor readings, and traffic or accident data derived from a variety ofsources.

FIG. 12 is an example of a flow diagram configured to control anautonomous vehicle, according to some embodiments. At 1202, flow 1200begins. Data representing a subset of objects that are received at aplanner in an autonomous vehicle, the subset of objects including atleast one object associated with data representing a degree of certaintyfor a classification type. For example, perception engine data mayinclude metadata associated with objects, whereby the metadata specifiesa degree of certainty associated with a specific classification type.For instance, a dynamic object may be classified as a “young pedestrian”with an 85% confidence level of being correct. At 1204, localizer datamay be received (e.g., at a planner). The localizer data may include mapdata that is generated locally within the autonomous vehicle. The localmap data may specify a degree of certainty (including a degree ofuncertainty) that an event at a geographic region may occur. An eventmay be a condition or situation affecting operation, or potentiallyaffecting operation, of an autonomous vehicle. The events may beinternal (e.g., failed or impaired sensor) to an autonomous vehicle, orexternal (e.g., roadway obstruction). Examples of events are describedherein, such as in FIG. 2 as well as in other figures and passages. Apath coextensive with the geographic region of interest may bedetermined at 1206. For example, consider that the event is thepositioning of the sun in the sky at a time of day in which theintensity of sunlight impairs the vision of drivers during rush hourtraffic. As such, it is expected or predicted that traffic may slow downresponsive to the bright sunlight. Accordingly, a planner maypreemptively invoke teleoperations if an alternate path to avoid theevent is less likely. At 1208, a local position is determined at aplanner based on local pose data. At 1210, a state of operation of anautonomous vehicle may be determined (e.g., probabilistically), forexample, based on a degree of certainty for a classification type and adegree of certainty of the event, which is may be based on any number offactors, such as speed, position, and other state information. Toillustrate, consider an example in which a young pedestrian is detectedby the autonomous vehicle during the event in which other drivers'vision likely will be impaired by the sun, thereby causing an unsafesituation for the young pedestrian. Therefore, a relatively unsafesituation can be detected as a probabilistic event that may be likely tooccur (i.e., an unsafe situation for which teleoperations may beinvoked). At 1212, a likelihood that the state of operation is in anormative state is determined, and based on the determination, a messageis transmitted to a teleoperator computing device requestingteleoperations to preempt a transition to a next state of operation(e.g., preempt transition from a normative to non-normative state ofoperation, such as an unsafe state of operation).

FIG. 13 depicts an example in which a planner may generate a trajectory,according to some examples. Diagram 1300 includes a trajectory evaluator1320 and a trajectory generator 1324. Trajectory evaluator 1320 includesa confidence level generator 1322 and a teleoperator query messenger1329. As shown, trajectory evaluator 1320 is coupled to a perceptionengine 1366 to receive static map data 1301, and current and predictedobject state data 1303. Trajectory evaluator 1320 also receives localpose data 1305 from localizer 1368 and plan data 1307 from a globalplanner 1369. In one state of operation (e.g., non-normative),confidence level generator 1322 receives static map data 1301 andcurrent and predicted object state data 1303. Based on this data,confidence level generator 1322 may determine that detected trajectoriesare associated with unacceptable confidence level values. As such,confidence level generator 1322 transmits detected trajectory data 1309(e.g., data including candidate trajectories) to notify a teleoperatorvia teleoperator query messenger 1329, which, in turn, transmits arequest 1370 for teleoperator assistance.

In another state of operation (e.g., a normative state), static map data1301, current and predicted object state data 1303, local pose data1305, and plan data 1307 (e.g., global plan data) are received intotrajectory calculator 1325, which is configured to calculate (e.g.,iteratively) trajectories to determine an optimal one or more paths.Next, at least one path is selected and is transmitted as selected pathdata 1311. According to some embodiments, trajectory calculator 1325 isconfigured to implement re-planning of trajectories as an example.Nominal driving trajectory generator 1327 is configured to generatetrajectories in a refined approach, such as by generating trajectoriesbased on receding horizon control techniques. Nominal driving trajectorygenerator 1327 subsequently may transmit nominal driving trajectory pathdata 1372 to, for example, a trajectory tracker or a vehicle controllerto implement physical changes in steering, acceleration, and othercomponents.

FIG. 14 is a diagram depicting another example of an autonomous vehicleservice platform, according to some embodiments. Diagram 1400 depicts anautonomous vehicle service platform 1401 including a teleoperatormanager 1407 that is configured to manage interactions and/orcommunications among teleoperators 1408, teleoperator computing devices1404, and other components of autonomous vehicle service platform 1401.Further to diagram 1400, autonomous vehicle service platform 1401includes a simulator 1440, a repository 1441, a policy manager 1442, areference data updater 1438, a 2D map data repository 1420, a 3D mapdata repository 1422, and a route data repository 1424. Other map data,such as 4D map data (e.g., using epoch determination), may beimplemented and stored in a repository (not shown).

Teleoperator action recommendation controller 1412 includes logicconfigured to receive and/or control a teleoperation service request viaautonomous vehicle (“AV”) planner data 1472, which can include requestsfor teleoperator assistance as well as telemetry data and other data. Assuch, planner data 1472 may include recommended candidate trajectoriesor paths from which a teleoperator 1408 via teleoperator computingdevice 1404 may select. According to some examples, teleoperator actionrecommendation controller 1412 may be configured to access other sourcesof recommended candidate trajectories from which to select an optimumtrajectory. For example, candidate trajectories contained in autonomousvehicle planner data 1472 may, in parallel, be introduced into simulator1440, which is configured to simulate an event or condition beingexperienced by an autonomous vehicle requesting teleoperator assistance.Simulator 1440 can access map data and other data necessary forperforming a simulation on the set of candidate trajectories, wherebysimulator 1440 need not exhaustively reiterate simulations to confirmsufficiency. Rather, simulator 1440 may provide either confirm theappropriateness of the candidate trajectories, or may otherwise alert ateleoperator to be cautious in their selection.

Teleoperator interaction capture analyzer 1416 may be configured tocapture numerous amounts of teleoperator transactions or interactionsfor storage in repository 1441, which, for example, may accumulate datarelating to a number of teleoperator transactions for analysis andgeneration of policies, at least in some cases. According to someembodiments, repository 1441 may also be configured to store policy datafor access by policy manager 1442. Further, teleoperator interactioncapture analyzer 1416 may apply machine learning techniques toempirically determine how best to respond to events or conditionscausing requests for teleoperation assistance. In some cases, policymanager 1442 may be configured to update a particular policy or generatea new policy responsive to analyzing the large set of teleoperatorinteractions (e.g., subsequent to applying machine learning techniques).Policy manager 1442 manages policies that may be viewed as rules orguidelines with which an autonomous vehicle controller and itscomponents operate under to comply with autonomous operations of avehicle. In some cases, a modified or updated policy may be applied tosimulator 1440 to confirm the efficacy of permanently releasing orimplementing such policy changes.

Simulator interface controller 1414 is configured to provide aninterface between simulator 1440 and teleoperator computing devices1404. For example, consider that sensor data from a fleet of autonomousvehicles is applied to reference data updater 1438 via autonomous (“AV”)fleet data 1470, whereby reference data updater 1438 is configured togenerate updated map and route data 1439. In some implementations,updated map and route data 1439 may be preliminarily released as anupdate to data in map data repositories 1420 and 1422, or as an updateto data in route data repository 1424. In this case, such data may betagged as being a “beta version” in which a lower threshold forrequesting teleoperator service may be implemented when, for example, amap tile including preliminarily updated information is used by anautonomous vehicle. Further, updated map and route data 1439 may beintroduced to simulator 1440 for validating the updated map data. Uponfull release (e.g., at the close of beta testing), the previouslylowered threshold for requesting a teleoperator service related to maptiles is canceled. User interface graphics controller 1410 provides richgraphics to teleoperators 1408, whereby a fleet of autonomous vehiclesmay be simulated within simulator 1440 and may be accessed viateleoperator computing device 1404 as if the simulated fleet ofautonomous vehicles were real.

FIG. 15 is an example of a flow diagram to control an autonomousvehicle, according to some embodiments. At 1502, flow 1500 begins.Message data may be received at a teleoperator computing device formanaging a fleet of autonomous vehicles. The message data may indicateevent attributes associated with a non-normative state of operation inthe context of a planned path for an autonomous vehicle. For example, anevent may be characterized as a particular intersection that becomesproblematic due to, for example, a large number of pedestrians,hurriedly crossing the street against a traffic light. The eventattributes describe the characteristics of the event, such as, forexample, the number of people crossing the street, the traffic delaysresulting from an increased number of pedestrians, etc. At 1504, ateleoperation repository may be accessed to retrieve a first subset ofrecommendations based on simulated operations of aggregated dataassociated with a group of autonomous vehicles. In this case, asimulator may be a source of recommendations with which a teleoperatormay implement. Further, the teleoperation repository may also beaccessed to retrieve a second subset of recommendations based on anaggregation of teleoperator interactions responsive to similar eventattributes. In particular, a teleoperator interaction capture analyzermay apply machine learning techniques to empirically determine how bestto respond to events having similar attributes based on previousrequests for teleoperation assistance. At 1506, the first subset and thesecond subset of recommendations are combined to form a set ofrecommended courses of action for the autonomous vehicle. At 1508,representations of the set of recommended courses of actions may bepresented visually on a display of a teleoperator computing device. At1510, data signals representing a selection (e.g., by teleoperator) of arecommended course of action may be detected.

FIG. 16 is a diagram of an example of an autonomous vehicle fleetmanager implementing a fleet optimization manager, according to someexamples. Diagram 1600 depicts an autonomous vehicle fleet manager thatis configured to manage a fleet of autonomous vehicles 1630 transitingwithin a road network 1650. Autonomous vehicle fleet manager 1603 iscoupled to a teleoperator 1608 via a teleoperator computing device 1604,and is also coupled to a fleet management data repository 1646.Autonomous vehicle fleet manager 1603 is configured to receive policydata 1602 and environmental data 1606, as well as other data. Further todiagram 1600, fleet optimization manager 1620 is shown to include atransit request processor 1631, which, in turn, includes a fleet dataextractor 1632 and an autonomous vehicle dispatch optimizationcalculator 1634. Transit request processor 1631 is configured to processtransit requests, such as from a user 1688 who is requesting autonomousvehicle service. Fleet data extractor 1632 is configured to extract datarelating to autonomous vehicles in the fleet. Data associated with eachautonomous vehicle is stored in repository 1646. For example, data foreach vehicle may describe maintenance issues, scheduled service calls,daily usage, battery charge and discharge rates, and any other data,which may be updated in real-time, may be used for purposes ofoptimizing a fleet of autonomous vehicles to minimize downtime.Autonomous vehicle dispatch optimization calculator 1634 is configuredto analyze the extracted data and calculate optimized usage of the fleetso as to ensure that the next vehicle dispatched, such as from station1652, provides for the least travel times and/or costs—in theaggregate—for the autonomous vehicle service.

Fleet optimization manager 1620 is shown to include a hybrid autonomousvehicle/non-autonomous vehicle processor 1640, which, in turn, includesan 10 AV/non-AV optimization calculator 1642 and a non-AV selector 1644.According to some examples, hybrid autonomous vehicle/non-autonomousvehicle processor 1640 is configured to manage a hybrid fleet ofautonomous vehicles and human-driven vehicles (e.g., as independentcontractors). As such, autonomous vehicle service may employnon-autonomous vehicles to meet excess demand, or in areas, such asnon-AV service region 1690, that may be beyond a geo-fence or in areasof poor communication coverage. AV/non-AV optimization calculator 1642is configured to optimize usage of the fleet of autonomous and to invitenon-AV drivers into the transportation service (e.g., with minimal or nodetriment to the autonomous vehicle service). Non-AV selector 1644includes logic for selecting a number of non-AV drivers to assist basedon calculations derived by AV/non-AV optimization calculator 1642.

FIG. 17 is an example of a flow diagram to manage a fleet of autonomousvehicles, according to some embodiments. At 1702, flow 1700 begins. At1702, policy data is received. The policy data may include parametersthat define how best apply to select an autonomous vehicle for servicinga transit request. At 1704, fleet management data from a repository maybe extracted. The fleet management data includes subsets of data for apool of autonomous vehicles (e.g., the data describes the readiness ofvehicles to service a transportation request). At 1706, datarepresenting a transit request is received. For exemplary purposes, thetransit request could be for transportation from a first geographiclocation to a second geographic location. At 1708, attributes based onthe policy data are calculated to determine a subset of autonomousvehicles that are available to service the request. For example,attributes may include a battery charge level and time until nextscheduled maintenance. At 1710, an autonomous vehicle is selected astransportation from the first geographic location to the secondgeographic location, and data is generated to dispatch the autonomousvehicle to a third geographic location associated with the originationof the transit request.

FIG. 18 is a diagram illustrating an autonomous vehicle fleet managerimplementing an autonomous vehicle communications link manager,according to some embodiments. Diagram 1800 depicts an autonomousvehicle fleet manager that is configured to manage a fleet of autonomousvehicles 1830 transiting within a road network 1850 that coincides witha communication outage at an area identified as “reduced communicationregion” 1880. Autonomous vehicle fleet manager 1803 is coupled to ateleoperator 1808 via a teleoperator computing device 1804. Autonomousvehicle fleet manager 1803 is configured to receive policy data 1802 andenvironmental 15 data 1806, as well as other data. Further to diagram1800, an autonomous vehicle communications link manager 1820 is shown toinclude an environment event detector 1831, a policy adaptiondeterminator 1832, and a transit request processor 1834. Environmentevent detector 1831 is configured to receive environmental data 1806specifying a change within the environment in which autonomous vehicleservice is implemented. For example, environmental data 1806 may specifythat region 1880 has degraded communication services, which may affectthe autonomous vehicle service. Policy adaption determinator 1832 mayspecify parameters with which to apply when receiving transit requestsduring such an event (e.g., during a loss of communications). Transitrequest processor 1834 is configured to process transit requests in viewof the degraded communications. In this example, a user 1888 isrequesting autonomous vehicle service. Further, transit requestprocessor 1834 includes logic to apply an adapted policy for modifyingthe way autonomous vehicles are dispatched so to avoid complications dueto poor communications.

Communication event detector 1840 includes a policy download manager1842 and communications-configured (“COMM-configured”) AV dispatcher1844. Policy download manager 1842 is configured to provide autonomousvehicles 1830 an updated policy in view of reduced communications region1880, whereby the updated policy may specify routes to quickly exitregion 1880 if an autonomous vehicle enters that region. For example,autonomous vehicle 1864 may receive an updated policy moments beforedriving into region 1880. Upon loss of communications, autonomousvehicle 1864 implements the updated policy and selects route 1866 todrive out of region 1880 quickly. COMM-configured AV dispatcher 1844 maybe configured to identify points 1865 at which to park autonomousvehicles that are configured as relays to establishing a peer-to-peernetwork over region 1880. As such, COMM-configured AV dispatcher 1844 isconfigured to dispatch autonomous vehicles 1862 (without passengers) topark at locations 1865 for the purposes of operating as communicationtowers in a peer-to-peer ad hoc network.

FIG. 19 is an example of a flow diagram to determine actions forautonomous vehicles during an event, such as degraded or lostcommunications, according to some embodiments. At 1901, flow 1900begins. Policy data is received, whereby the policy data definesparameters with which to apply to transit requests in a geographicalregion during an event. At 1902, one or more of the following actionsmay be implemented: (1) dispatch a subset of autonomous vehicles togeographic locations in the portion of the geographic location, thesubset of autonomous vehicles being configured to either park atspecific geographic locations and each serve as a static communicationrelay, or transit in a geographic region to each serve as a mobilecommunication relay, (2) implement peer-to-peer communications among aportion of the pool of autonomous vehicles associated with the portionof the geographic region, (3) provide to the autonomous vehicles anevent policy that describes a route to egress the portion of thegeographic region during an event, (4) invoke teleoperations, and (5)recalculate paths so as to avoid the geographic portion. Subsequent toimplementing the action, the fleet of autonomous vehicles is monitoredat 1914.

FIG. 20 is a diagram depicting an example of a localizer, according tosome embodiments. Diagram 2000 includes a localizer 2068 configured toreceive sensor data from sensors 2070, such as Lidar data 2072, cameradata 2074, radar data 2076, and other data 2078. Further, localizer 2068is configured to receive reference data 5 2020, such as 2D map data2022, 3D map data 2024, and 3D local map data. According to someexamples, other map data, such as 4D map data 2025 and semantic map data(not shown), including corresponding data structures and repositories,may also be implemented. Further to diagram 2000, localizer 2068includes a positioning system 2010 and a localization system 2012, bothof which are configured to receive sensor data from sensors 2070 as wellas reference data 2020. Localization data integrator 2014 is configuredto receive data from positioning system 2010 and data from localizationsystem 2012, whereby localization data integrator 2014 is configured tointegrate or fuse sensor data from multiple sensors to form local posedata 2052.

FIG. 21 is an example of a flow diagram to generate local pose databased on integrated sensor data, according to some embodiments. At 2101,flow 2100 begins. At 2102, reference data is received, the referencedata including three dimensional map data. In some examples, referencedata, such as 3-D or 4-D map data, may be received via one or morenetworks. At 2104, localization data from one or more localizationsensors is received and placed into a localization system. At 2106,positioning data from one or more positioning sensors is received into apositioning system. At 2108, the localization and positioning data areintegrated. At 2110, the localization data and positioning data areintegrated to form local position data specifying a geographic positionof an autonomous vehicle.

FIG. 22 is a diagram depicting another example of a localizer, accordingto some embodiments. Diagram 2200 includes a localizer 2268, which, inturn, includes a localization system 2210 and a relative localizationsystem 2212 to generate positioning-based data 2250 and locallocation-based data 2251, respectively. Localization system 2210includes a projection processor 2254 a for processing GPS data 2273, aGPS datum 2211, and 3D Map data 2222, among other optional data (e.g.,4D map data). Localization system 2210 also includes an odometryprocessor 2254 b to process wheel data 2275 (e.g., wheel speed), vehiclemodel data 2213 and 3D map data 2222, among other optional data. Furtheryet, localization system 2210 includes an integrator processor 2254 c toprocess IMU data 2257, vehicle model data 2215, and 3D map data 2222,among other optional data. Similarly, relative localization system 2212includes a Lidar localization processor 2254 d for processing Lidar data2272, 2D tile map data 2220, 3D map data 2222, and 3D local map data2223, among other optional data. Relative localization system 2212 alsoincludes a visual registration processor 2254 e to process camera data2274, 3D map data 2222, and 3D local map data 2223, among other optionaldata. Further yet, relative localization system 2212 includes a radarreturn processor 2254 f to process radar data 2276, 3D map data 2222,and 3D local map data 2223, among other optional data. Note that invarious examples, other types of sensor data and sensors or processorsmay be implemented, such as sonar data and the like.

Further to diagram 2200, localization-based data 2250 and relativelocalization-based data 2251 may be fed into data integrator 2266 a andlocalization data integrator 2266, respectively. Data integrator 2266 aand localization data integrator 2266 may be configured to fusecorresponding data, whereby localization-based data 2250 may be fused atdata integrator 2266 a prior to being fused with relativelocalization-based data 2251 at localization data integrator 2266.According to some embodiments, data integrator 2266 a is formed as partof localization data integrator 2266, or is absent. Regardless, alocalization-based data 2250 and relative localization-based data 2251can be both fed into localization data integrator 2266 for purposes offusing data to generate local position data 2252. Localization-baseddata 2250 may include unary-constrained data (and uncertainty values)from projection processor 2254 a, as well as binary-constrained data(and uncertainty values) from odometry processor 2254 b and integratorprocessor 2254 c. Relative localization-based data 2251 may includeunary-constrained data (and uncertainty values) from localizationprocessor 2254 d and visual registration processor 2254 e, andoptionally from radar return processor 2254 f. According to someembodiments, localization data integrator 2266 may implement non-linearsmoothing functionality, such as a Kalman filter (e.g., a gated Kalmanfilter), a relative bundle adjuster, pose-graph relaxation, particlefilter, histogram filter, or the like.

FIG. 23 is a diagram depicting an example of a perception engine,according to some embodiments. Diagram 2300 includes a perception engine2366, which, in turn, includes a segmentation processor 2310, an objecttracker 2330, and a classifier 2360. Further, perception engine 2366 isconfigured to receive a local position data 2352, Lidar data 2372,camera data 2374, and radar data 2376, for example. Note that othersensor data, such as sonar data, may be accessed to providefunctionalities of perception engine 2366. Segmentation processor 2310is configured to extract ground plane data and/or to segment portions ofan image to distinguish objects from each other and from static imagery(e.g., background). In some cases, 3D blobs may be segmented todistinguish each other. In some examples, a blob may refer to a set offeatures that identify an object in a spatially-reproduced environmentand may be composed of elements (e.g., pixels of camera data, points oflaser return data, etc.) having similar characteristics, such asintensity and color. In some examples, a blob may also refer to a pointcloud (e.g., composed of colored laser return data) or other elementsconstituting an object. Object tracker 2330 is configured to performframe-to-frame estimations of motion for blobs, or other segmented imageportions. Further, data association is used to associate a blob at onelocation in a first frame at time, t1, to a blob in a different positionin a second frame at time, t2. In some examples, object tracker 2330 isconfigured to perform real-time probabilistic tracking of 3-D objects,such as blobs. Classifier 2360 is configured to identify an object andto classify that object by classification type (e.g., as a pedestrian,cyclist, etc.) and by energy/activity (e.g. whether the object isdynamic or static), whereby data representing classification isdescribed by a semantic label. According to some embodiments,probabilistic estimations of object categories may be performed, such asclassifying an object as a vehicle, bicyclist, pedestrian, etc. withvarying confidences per object class. Perception engine 2366 isconfigured to determine perception engine data 2354, which may includestatic object maps and/or dynamic object maps, as well as semanticinformation so that, for example, a planner may use this information toenhance path planning. According to various examples, one or more ofsegmentation processor 2310, object tracker 2330, and classifier 2360may apply machine learning techniques to generate perception engine data2354.

FIG. 24 is an example of a flow chart to generate perception enginedata, according to some embodiments. Flow chart 2400 begins at 2402, atwhich data representing a local position of an autonomous vehicle isretrieved. At 2404, localization data from one or more localizationsensors is received, and features of an environment in which theautonomous vehicle is disposed are segmented at 2406 to form segmentedobjects. One or more portions of the segmented object are trackedspatially at 2408 to form at least one tracked object having a motion(e.g., an estimated motion). At 2410, a tracked object is classified atleast as either being a static object or a dynamic object. In somecases, a static object or a dynamic object may be associated with aclassification type. At 2412, data identifying a classified object isgenerated. For example, the data identifying the classified object mayinclude semantic information.

FIG. 25 is an example of a segmentation processor, according to someembodiments. Diagram 2500 depicts a segmentation processor 2510receiving Lidar data from one or more Lidars 2572 and camera image datafrom one or more cameras 2574. Local pose data 2552, Lidar data, andcamera image data are received into meta spin generator 2521. In someexamples, meta spin generator is configured to partition an image basedon various attributes (e.g., color, intensity, etc.) intodistinguishable regions (e.g., clusters or groups of a point cloud), atleast two or more of which may be updated at the same time or about thesame time. Meta spin data 2522 is used to perform object segmentationand ground segmentation at segmentation processor 2523, whereby bothmeta spin data 2522 and segmentation-related data from segmentationprocessor 2523 are applied to a scanned differencing processor 2513.Scanned differencing processor 2513 is configured to predict motionand/or relative velocity of segmented image portions, which can be usedto identify dynamic objects at 2517. Data indicating objects withdetected velocity at 2517 are optionally transmitted to the planner toenhance path planning decisions. Additionally, data from scanneddifferencing processor 2513 may be used to approximate locations ofobjects to form mapping of such objects (as well as optionallyidentifying a level of motion). In some examples, an occupancy grid map2515 may be generated. Data representing an occupancy grid map 2515 maybe transmitted to the planner to further enhance path planning decisions(e.g., by reducing uncertainties). Further to diagram 2500, image cameradata from one or more cameras 2574 are used to classify blobs in blobclassifier 2520, which also receives blob data 2524 from segmentationprocessor 2523. Segmentation processor 2510 also may receive raw radarreturns data 2512 from one or more radars 2576 to perform segmentationat a radar segmentation processor 2514, which generates radar-relatedblob data 2516. Further to 10 FIG. 25, segmentation processor 2510 mayalso receive and/or generate tracked blob data 2518 related to radardata. Blob data 2516, tracked blob data 2518, data from blob classifier2520, and blob data 2524 may be used to track objects or portionsthereof. According to some examples, one or more of the following may beoptional: scanned differencing processor 2513, blob classification 2520,and data from radar 2576.

FIG. 26A is a diagram depicting examples of an object tracker and aclassifier, according to various embodiments. Object tracker 2630 ofdiagram 2600 is configured to receive blob data 2516, tracked blob data2518, data from blob classifier 2520, blob data 2524, and camera imagedata from one or more cameras 2676. Image tracker 2633 is configured toreceive camera image data from one or more cameras 2676 to generatetracked image data, which, in turn, may be provided to data associationprocessor 2632. As shown, data association processor 2632 is configuredto receive blob data 2516, tracked blob data 2518, data from blobclassifier 2520, blob data 2524, and track image data from image tracker2633, and is further configured to identify one or more associationsamong the above-described types of data. Data association processor 2632is configured to track, for example, various blob data from one frame toa next frame to, for example, estimate motion, among other things.Further, data generated by data association processor 2632 may be usedby track updater 2634 to update one or more tracks, or tracked objects.In some examples, track updater 2634 may implement a Kalman Filter, orthe like, to form updated data for tracked objects, which may be storedonline in track database (“DB”) 2636. Feedback data may be exchanged viapath 2699 between data association processor 2632 and track database2636. In some examples, image tracker 2633 may be optional and may beexcluded. Object tracker 2630 may also use other sensor data, such asradar or sonar, as well as any other types of sensor data, for example.

FIG. 26B is a diagram depicting another example of an object trackeraccording to at least some examples. Diagram 2601 includes an objecttracker 2631 that may include structures and/or functions assimilarly-named elements described in connection to one or more otherdrawings (e.g., FIG. 26A). As shown, object tracker 2631 includes anoptional registration portion 2699 that includes a processor 2696configured to perform object scan registration and data fusion.Processor 2696 is further configured to store the resultant data in 3Dobject database 2698.

Referring back to FIG. 26A, diagram 2600 also includes classifier 2660,which may include a track classification engine 2662 for generatingstatic obstacle data 2672 and dynamic obstacle data 2674, both of whichmay be transmitted to the planner for path planning purposes. In atleast one example, track classification engine 2662 is configured todetermine whether an obstacle is static or dynamic, as well as anotherclassification type for the object (e.g., whether the object is avehicle, pedestrian, tree, cyclist, dog, cat, paper bag, etc.). Staticobstacle data 2672 may be formed as part of an obstacle map (e.g., a 2Doccupancy map), and dynamic obstacle data 2674 may be formed to includebounding boxes with data indicative of velocity and classification type.Dynamic obstacle data 2674, at least in some cases, includes 2D dynamicobstacle map data.

FIG. 27 is an example of front-end processor for a perception engine,according to some examples. Diagram 2700 includes a ground segmentationprocessor 2723 a for performing ground segmentation, and an oversegmentation processor 2723 b for performing “over-segmentation,”according to various examples. Processors 2723 a and 2723 b areconfigured to receive optionally colored Lidar data 2775. Oversegmentation processor 2723 b generates data 2710 of a first blob type(e.g., a relatively small blob), which is provided to an aggregationclassification and segmentation engine 2712 that generates data 2714 ofa second blob type. Data 2714 is provided to data association processor2732, which is configured to detect whether data 2714 resides in trackdatabase 2736. A determination is made at 2740 whether data 2714 of thesecond blob type (e.g., a relatively large blob, which may include oneor more smaller blobs) is a new track. If so, a track is initialized at2742, otherwise, the tracked object data stored in track database 2736and the track may be extended or updated by track updater 2742. Trackclassification engine 2762 is coupled to track database 2736 to identifyand update/modify tracks by, for example, adding, removing or modifyingtrack-related data.

FIG. 28 is a diagram depicting a simulator configured to simulate anautonomous vehicle in a synthetic environment, according to variousembodiments. Diagram 2800 includes a simulator 2840 that is configuredto generate a simulated environment 2803. As shown, simulator 2840 isconfigured to use reference data 2822 (e.g., 3D map data and/or othermap or route data including RNDF data or similar road network data) togenerate simulated geometries, such as simulated surfaces 2892 a and2892 b, within simulated environment 2803. Simulated surfaces 2892 a and2892 b may simulate walls or front sides of buildings adjacent aroadway. Simulator 2840 may also pre-generated or procedurally generateduse dynamic object data 2825 to simulate dynamic agents in a syntheticenvironment. An example of a dynamic agent is simulated dynamic object2801, which is representative of a simulated cyclist having a velocity.The simulated dynamic agents may optionally respond to other static anddynamic agents in the simulated environment, including the simulatedautonomous vehicle. For example, simulated object 2801 may slow down forother obstacles in simulated environment 2803 rather than follow apreset trajectory, thereby creating a more realistic simulation ofactual dynamic environments that exist in the real world.

Simulator 2840 may be configured to generate a simulated autonomousvehicle controller 2847, which includes synthetic adaptations of aperception engine 2866, a localizer 2868, a motion controller 2862, anda planner 2864, each of which may have functionalities described hereinwithin simulated environment 2803. Simulator 2840 may also generatesimulated interfaces (“I/F”) 2849 to simulate the data exchanges withdifferent sensors modalities and different sensor data formats. As such,simulated interface 2849 may simulate a software interface forpacketized data from, for example, a simulated Lidar sensor 2872.Further, simulator 2840 may also be configured to generate a simulatedautonomous vehicle 2830 that implements simulated AV controller 2847.Simulated autonomous vehicle 2830 includes simulated Lidar sensors 2872,simulated camera or image sensors 2874, and simulated radar sensors2876. In the example shown, simulated Lidar sensor 2872 may beconfigured to generate a simulated laser consistent with ray trace 2892,which causes generation of simulated sensor return 2891. Note thatsimulator 2840 may simulate the addition of noise or other environmentaleffects on sensor data (e.g., added diffusion or reflections that affectsimulated sensor return 2891, etc.). Further yet, simulator 2840 may beconfigured to simulate a variety of sensor defects, including sensorfailure, sensor miscalibration, intermittent data outages, and the like.

Simulator 2840 includes a physics processor 2850 for simulating themechanical, static, dynamic, and kinematic aspects of an autonomousvehicle for use in simulating behavior of simulated autonomous vehicle2830. For example, physics processor 2850 includes a content mechanicsmodule 2851 for simulating contact mechanics, a collision detectionmodule 2852 for simulating the interaction between simulated bodies, anda multibody dynamics module 2854 to simulate the interaction betweensimulated mechanical interactions.

Simulator 2840 also includes a simulator controller 2856 configured tocontrol the simulation to adapt the functionalities of anysynthetically-generated element of simulated environment 2803 todetermine cause-effect relationship, among other things. Simulator 2840includes a simulator evaluator 2858 to evaluate the performancesynthetically-generated element of simulated environment 2803. Forexample, simulator evaluator 2858 may analyze simulated vehicle commands2880 (e.g., simulated steering angles and simulated velocities) todetermine whether such commands are an appropriate response to thesimulated activities within simulated environment 2803. Further,simulator evaluator 2858 may evaluate interactions of a teleoperator2808 with the simulated autonomous vehicle 2830 via teleoperatorcomputing device 2804. Simulator evaluator 2858 may evaluate the effectsof updated reference data 2827, including updated map tiles and routedata, which may be added to guide the responses of simulated autonomousvehicle 2830. Simulator evaluator 2858 may also evaluate the responsesof simulator AV controller 2847 when policy data 2829 is updated,deleted, or added. The above-description of simulator 2840 is notintended to be limiting. As such, simulator 2840 is configured toperform a variety of different simulations of an autonomous vehiclerelative to a simulated environment, which include both static anddynamic features. For example, simulator 2840 may be used to validatechanges in software versions to ensure reliability. Simulator 2840 mayalso be used to determine vehicle dynamics properties and forcalibration purposes. Further, simulator 2840 may be used to explore thespace of applicable controls and resulting trajectories so as to effectlearning by self-simulation.

FIG. 29 is an example of a flow chart to simulate various aspects of anautonomous vehicle, according to some embodiments. Flow chart 2900begins at 2902, at which reference data including three dimensional mapdata is received into a simulator. Dynamic object data defining motionpatterns for a classified object may be retrieved at 2904. At 2906, asimulated environment is formed based on at least three dimensional 20(“3D”) map data and the dynamic object data. The simulated environmentmay include one or more simulated surfaces. At 2908, an autonomousvehicle is simulated that includes a simulated autonomous vehiclecontroller that forms part of a simulated environment. The autonomousvehicle controller may include a simulated perception engine and asimulated localizer configured to receive sensor data. At 2910,simulated sensor data are generated based on data for at least onesimulated sensor return, and simulated vehicle commands are generated at2912 to cause motion (e.g., vectored propulsion) by a simulatedautonomous vehicle in a synthetic environment. At 2914, simulatedvehicle commands are evaluated to determine whether the simulatedautonomous vehicle behaved consistent with expected behaviors (e.g.,consistent with a policy).

FIG. 30 is an example of a flow chart to generate map data, according tosome embodiments. Flow chart 3000 begins at 3002, at which trajectorydata is retrieved. The trajectory data may include trajectories capturedover a duration of time (e.g., as logged trajectories). At 3004, atleast localization data may be received. The localization data may becaptured over a duration of time (e.g., as logged localization data). At3006, a camera or other image sensor may be implemented to generate asubset of the localization data. As such, the retrieved localizationdata may include image data. At 3008, subsets of localization data arealigned to identifying a global position (e.g., a global pose). At 3010,three dimensional (“3D”) map data is generated based on the globalposition, and at 3012, the 3 dimensional map data is available forimplementation by, for example, a manual route data editor (e.g.,including a manual road network data editor, such as an RNDF editor), anautomated route data generator (e.g., including an automatic roadnetwork generator, including an automatic RNDF generator), a fleet ofautonomous vehicles, a simulator, a teleoperator computing device, andany other component of an autonomous vehicle service.

FIG. 31 is a diagram depicting an architecture of a mapping engine,according to some embodiments. Diagram 3100 includes a 3D mapping enginethat is configured to receive trajectory log data 3140, Lidar log data3172, camera log data 3174, radar log data 3176, and other optionallogged sensor data (not shown). Logic 3141 includes a loop-closuredetector 3150 configured to detect whether sensor data indicates anearby point in space has been previously visited, among other things.Logic 3141 also includes a registration controller 3152 for aligning mapdata, including 3D map data in some cases, relative to one or moreregistration points. Further, logic 3141 provides data 3142 representingstates of loop closures for use by a global pose graph generator 3143,which is configured to generate pose graph data 3145. In some examples,pose graph data 3145 may also be generated based on data fromregistration refinement module 3146. Logic 3144 includes a 3D mapper3154 and a Lidar self-calibration unit 3156. Further, logic 3144receives sensor data and pose graph data 3145 to generate 3D map data3120 (or other map data, such as 4D map data). In some examples, logic3144 may implement a truncated sign distance function (“TSDF”) to fusesensor data and/or map data to form optimal three-dimensional maps.Further, logic 3144 is configured to include texture and reflectanceproperties. 3D map data 3120 may be released for usage by a manual routedata editor 3160 (e.g., an editor to manipulate Route data or othertypes of route or reference data), an automated route data generator3162 (e.g., logic to configured to generate route data or other types ofroad network or reference data), a fleet of autonomous vehicles 3164, asimulator 3166, a teleoperator computing device 3168, 10 and any othercomponent of an autonomous vehicle service. Mapping engine 3110 maycapture semantic information from manual annotation orautomatically-generated annotation as well as other sensors, such assonar or instrumented environment (e.g., smart stop-lights).

FIG. 32 is a diagram depicting an autonomous vehicle application,according to some examples. Diagram 3200 depicts a mobile computingdevice 3203 including an autonomous service application 3240 that isconfigured to contact an autonomous vehicle service platform 3201 toarrange transportation of user 3202 via an autonomous vehicle 3230. Asshown, autonomous service application 3240 may include a transportationcontroller 3242, which may be a software application residing on acomputing device (e.g., a mobile phone 3203, etc.). Transportationcontroller 3242 is configured to receive, schedule, select, or performoperations related to autonomous vehicles and/or autonomous vehiclefleets for which a user 3202 may arrange transportation from the user'slocation to a destination. For example, user 3202 may open up anapplication to request vehicle 3230. The application may display a mapand user 3202 may drop a pin to indicate their destination within, forexample, a geo-fenced region. Alternatively, the application may displaya list of nearby pre-specified pick-up locations, or provide the userwith a text entry field in which to type a destination either by addressor by name.

Further to the example shown, autonomous vehicle application 3240 mayalso include a user identification controller 3246 that may beconfigured to detect that user 3202 is in a geographic region, orvicinity, near autonomous vehicle 3230, as the vehicle approaches. Insome situations, user 3202 may not readily perceive or identifyautonomous vehicle 3230 as it approaches for use by user 3203 (e.g., dueto various other vehicles, including trucks, cars, taxis, and otherobstructions that are typical in city environments). In one example,autonomous vehicle 3230 may establish a wireless communication link 3262(e.g., via a radio frequency (“RF”) signal, such as WiFi or Bluetooth®,including BLE, or the like) for communicating and/or determining aspatial location of user 3202 relative to autonomous vehicle 3230 (e.g.,using relative direction of RF signal and signal strength). In somecases, autonomous vehicle 3230 may detect an approximate geographiclocation of user 3202 using, for example, GPS data or the like. A GPSreceiver (not shown) of mobile computing device 3203 may be configuredto provide GPS data to autonomous vehicle service application 3240.Thus, user identification controller 3246 may provide GPS data via link3260 to autonomous vehicle service platform 3201, which, in turn, mayprovide that location to autonomous vehicle 3230 via link 3261.Subsequently, autonomous vehicle 3230 may determine a relative distanceand/or direction of user 3202 by comparing the user's GPS data to thevehicle's GPS-derived location.

Autonomous vehicle 3230 may also include additional logic to identifythe presence of user 3202, such that logic configured to perform facedetection algorithms to detect either user 3202 generally, or tospecifically identify the identity (e.g., name, phone number, etc.) ofuser 3202 based on the user's unique facial characteristics. Further,autonomous vehicle 3230 may include logic to detect codes foridentifying user 25 3202. Examples of such codes include specializedvisual codes, such as QR codes, color codes, etc., specialized audiocodes, such as voice activated or recognized codes, etc., and the like.In some cases, a code may be an encoded security key that may betransmitted digitally via link 3262 to autonomous vehicle 3230 to ensuresecure ingress and/or egress. Further, one or more of theabove-identified techniques for identifying user 3202 may be used as asecured means to grant ingress and egress privileges to user 3202 so asto prevent others from entering autonomous vehicle 3230 (e.g., to ensurethird party persons do not enter an unoccupied autonomous vehicle priorto arriving at user 3202). According to various examples, any othermeans for identifying user 3202 and providing secured ingress and egressmay also be implemented in one or more of autonomous vehicle serviceapplication 3240, autonomous vehicle service platform 3201, andautonomous vehicle 3230.

To assist user 3302 in identifying the arrival of its requestedtransportation, autonomous vehicle 3230 may be configured to notify orotherwise alert user 3202 to the presence of autonomous vehicle 3230 asit approaches user 3202. For example, autonomous vehicle 3230 mayactivate one or more light-emitting devices 3280 (e.g., LEDs) inaccordance with specific light patterns. In particular, certain lightpatterns are created so that user 3202 may readily perceive thatautonomous vehicle 3230 is reserved to service the transportation needsof user 3202. As an example, autonomous 15 vehicle 3230 may generatelight patterns 3290 that may be perceived by user 3202 as a “wink,” orother animation of its exterior and interior lights in such a visual andtemporal way. The patterns of light 3290 may be generated with orwithout patterns of sound to identify to user 3202 that this vehicle isthe one that they booked.

According to some embodiments, autonomous vehicle user controller 3244may implement a software application that is configured to controlvarious functions of an autonomous vehicle. Further, an application maybe configured to redirect or reroute the autonomous vehicle duringtransit to its initial destination. Further, autonomous vehicle usercontroller 3244 may be configured to cause on-board logic to modifyinterior lighting of autonomous vehicle 3230 to effect, for example,mood lighting. Controller 3244 may also control a source of audio (e.g.,an external source such as Spotify, or audio stored locally on themobile computing device 3203), select a type of ride (e.g., modifydesired acceleration and braking aggressiveness, modify activesuspension parameters to select a set of “road-handling” characteristicsto implement aggressive driving characteristics, including vibrations,or to select “soft-ride” qualities with vibrations dampened forcomfort), and the like. For example, mobile computing device 3203 may beconfigured to control HVAC functions as well, like ventilation andtemperature.

FIGS. 33 to 35 illustrate examples of various computing platformsconfigured to provide various functionalities to components of anautonomous vehicle service, according to various embodiments. In someexamples, computing platform 3300 may be used to implement computerprograms, applications, methods, processes, algorithms, or othersoftware to perform the above-described techniques.

Note that various structures and/or functionalities of FIG. 33 areapplicable to FIGS. 34 and 35, and, as such, some elements in thosefigures may be discussed in the context of FIG. 33.

In some cases, computing platform 3300 can be disposed in any device,such as a computing device 3390 a, which may be disposed in one or morecomputing devices in an autonomous vehicle service platform, anautonomous vehicle 3391, and/or mobile computing device 3390 b.

Computing platform 3300 includes a bus 3302 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 3304, system memory 3306 (e.g., RAM,etc.), storage device 3308 (e.g., ROM, etc.), an in-memory cache (whichmay be implemented in RAM 3306 or other portions of computing platform3300), a communication interface 3313 (e.g., an Ethernet or wirelesscontroller, a Bluetooth controller, NFC logic, etc.) to facilitatecommunications via a port on communication link 3321 to communicate, forexample, with a computing device, including mobile computing and/orcommunication devices with processors. Processor 3304 can be implementedwith one or more graphics processing units (“GPUs”), with one or morecentral processing units (“CPUs”), such as those manufactured by Intel®Corporation, or one or more virtual processors, as well as anycombination of CPUs and virtual processors. Computing platform 3300exchanges data representing inputs and outputs via input-and-outputdevices 3301, including, but not limited to, keyboards, mice, audioinputs (e.g., speech-to-text devices), user interfaces, displays,monitors, cursors, touch-sensitive displays, LCD or LED displays, andother I/O-related devices.

According to some examples, computing platform 3300 performs specificoperations by processor 3304 executing one or more sequences of one ormore instructions stored in system memory 3306, and computing platform3300 can be implemented in a client-server arrangement, peer-to-peerarrangement, or as any mobile computing device, including smart phonesand the like. Such instructions or data may be read into system memory3306 from another computer readable medium, such as storage device 3308.In some examples, hard-wired circuitry may be used in place of or incombination with software instructions for implementation. Instructionsmay be embedded in software or firmware. The term “computer readablemedium” refers to any tangible medium that participates in providinginstructions to processor 3304 for execution. Such a medium may takemany forms, including but not limited to, nonvolatile media and volatilemedia. Non-volatile media includes, for example, optical or magneticdisks and the like. Volatile media includes dynamic memory, such assystem memory 3306.

Common forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CDROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can read. Instructions may further be transmittedor received using a transmission medium. The term “transmission medium”may include any tangible or intangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machine,and includes digital or analog communications signals or otherintangible medium to facilitate communication of such instructions.Transmission media includes coaxial cables, copper wire, and fiberoptics, including wires that comprise bus 3302 for transmitting acomputer data signal.

In some examples, execution of the sequences of instructions may beperformed by computing platform 3300. According to some examples,computing platform 3300 can be coupled by communication link 3321 (e.g.,a wired network, such as LAN, PSTN, or any wireless network, includingWiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.)to any other processor to perform the sequence of instructions incoordination with (or asynchronous to) one another. Computing platform3300 may transmit and receive messages, data, and instructions,including program code (e.g., application code) through communicationlink 3321 and communication interface 3313. Received program code may beexecuted by processor 3304 as it is received, and/or stored in memory3306 or other non-volatile storage for later execution.

In the example shown, system memory 3306 can include various modulesthat include executable instructions to implement functionalitiesdescribed herein. System memory 3306 may include an operating system(“O/S”) 3332, as well as an application 3336 and/or logic module(s)3359. In the example shown in FIG. 33, system memory 3306 includes anautonomous vehicle (“AV”) controller module 3350 and/or its components(e.g., a perception engine module, a localization module, a plannermodule, and/or a motion controller module), any of which, or one or moreportions of which, can be configured to facilitate an autonomous vehicleservice by implementing one or more functions described herein.

Referring to the example shown in FIG. 34, system memory 3306 20includes an autonomous vehicle service platform module 3450 and/or itscomponents (e.g., a teleoperator manager, a simulator, etc.), any ofwhich, or one or more portions of which, can be configured to facilitatemanaging an autonomous vehicle service by implementing one or morefunctions described herein.

Referring to the example shown in FIG. 35, system memory 3306 includesan autonomous vehicle (“AV”) module and/or its components for use, forexample, in a mobile computing device. One or more portions of module3550 can be configured to facilitate delivery of an autonomous vehicleservice by implementing one or more functions described herein.

Referring back to FIG. 33, the structures and/or functions of any of theabove-described features can be implemented in software, hardware,firmware, circuitry, or a combination thereof. Note that the structuresand constituent elements above, as well as their functionality, may beaggregated with one or more other structures or elements. Alternatively,the elements and their functionality may be subdivided into constituentsub-elements, if any. As software, the above-described techniques may beimplemented using various types of programming or formatting languages,frameworks, syntax, applications, protocols, objects, or techniques. Ashardware and/or firmware, the above-described techniques may beimplemented using various types of programming or integrated circuitdesign languages, including hardware description languages, such as anyregister transfer language (“RTL”) configured to designfield-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), or any other type of integrated circuit.According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof. These can bevaried and are not limited to the examples or descriptions provided.

In some embodiments, module 3350 of FIG. 33, module 3450 of FIG. 34, andmodule 3550 of FIG. 35, or one or more of their components, or anyprocess or device described herein, can be in communication (e.g., wiredor wirelessly) with a mobile device, such as a mobile phone or computingdevice, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (notshown) in communication with one or more modules 3359 (module 3350 ofFIG. 33, module 3450 of FIG. 34, and module 3550 of FIG. 35) or one ormore of its components (or any process or device described herein), canprovide at least some of the structures and/or functions of any of thefeatures described herein. As depicted in the above-described figures,the structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or anycombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated or combinedwith one or more other structures or elements. Alternatively, theelements and their functionality may be subdivided into constituentsub-elements, if any. As software, at least some of the above-describedtechniques may be implemented using various types of programming orformatting languages, frameworks, syntax, applications, protocols,objects, or techniques. For example, at least one of the elementsdepicted in any of the figures can represent one or more algorithms. Or,at least one of the elements can represent a portion of logic includinga portion of hardware configured to provide constituent structuresand/or functionalities.

For example, module 3350 of FIG. 33, module 3450 of FIG. 34, and module3550 of FIG. 35, or one or more of its components, or any process ordevice described herein, can be implemented in one or more computingdevices (i.e., any mobile computing device, such as a wearable device,an audio device (such as headphones or a headset) or mobile phone,whether worn or carried) that include one or more processors configuredto execute one or more algorithms in memory. Thus, at least some of theelements in the above-described figures can represent one or morealgorithms. Or, at least one of the elements can represent a portion oflogic including a portion of hardware configured to provide constituentstructures and/or functionalities. These can be varied and are notlimited to the examples or descriptions provided.

As hardware and/or firmware, the above-described structures andtechniques can be implemented using various types of programming orintegrated circuit design languages, including hardware descriptionlanguages, such as any register transfer language (“RTL”) configured todesign field-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), multi-chip modules, or any other type ofintegrated circuit.

For example, module 3350 of FIG. 33, module 3450 of FIG. 34, and module3550 of FIG. 35, or one or more of its components, or any process ordevice described herein, can be implemented in one or more computingdevices that include one or more circuits. Thus, at least one of theelements in the above-described figures can represent one or morecomponents of hardware. Or, at least one of the elements can represent aportion of logic including a portion of a circuit configured to provideconstituent structures and/or functionalities.

According to some embodiments, the term “circuit” can refer, forexample, to any system including a number of components through whichcurrent flows to perform one or more functions, the components includingdiscrete and complex components. Examples of discrete components includetransistors, resistors, capacitors, inductors, diodes, and the like, andexamples of complex components include memory, processors, analogcircuits, digital circuits, and the like, including field-programmablegate arrays (“FPGAs”), application-specific integrated circuits(“ASICs”). Therefore, a circuit can include a system of electroniccomponents and logic components (e.g., logic configured to executeinstructions, such that a group of executable instructions of analgorithm, for example, and, thus, is a component of a circuit).According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof (i.e., a modulecan be implemented as a circuit). In some embodiments, algorithms and/orthe memory in which the algorithms are stored are “components” of acircuit. Thus, the term “circuit” can also refer, for example, to asystem of components, including algorithms. These can be varied and arenot limited to the examples or descriptions provided.

FIG. 36 is a diagram depicting an example of a teleoperator managerconfigured to at least influence navigational control of an autonomousvehicle, according to some examples. Diagram 3600 depicts a teleoperatormanager 3607 that generates data configured to guide generation oftrajectories at an autonomous vehicle 3630, where the guidance providedby teleoperator manager 3607 assists in facilitating navigation ofautonomous vehicle 3630. Teleoperator manager 3607 may include logic,such as hardware or software, or a combination thereof, and may bedisposed in a teleoperator computing device 3604 with whichteleoperators 3608 may interact. Therefore, a teleoperator 3608 maymonitor remotely, such as via a user interface or display ofteleoperator computing device 3604, a number of functions of autonomousvehicle 3630. Moreover, a teleoperator may visually discern an eventbased on view by autonomous vehicle 3630 similar to an occupant (e.g., ahigh definition, 3D view in 360 degrees or less, both horizontally andvertically) of the physical environment (e.g., roadway, cityscape, etc.)in which autonomous vehicle 3630 travels. In particular, the view may be5 a visualization of the environment relative to, for example, a leadingsurface of the autonomous vehicle 3630 in the direction of travel. Theteleoperator 3608 may provide guidance or assistance to autonomousvehicle 3630 and a fleet of autonomous vehicles 3630 a to assist innavigation or resolving any other event or condition, whether occurringexternal or internal to autonomous vehicle 3630.

Teleoperator manager 3607 is configured to facilitate a connection, orsession, via a communication channel over network 3606 between one ofteleoperators 3608 and autonomous vehicle 3630 responsive to receiving asignal to invoke teleoperations received from autonomous vehicle 3630,from within an autonomous vehicle service platform (not shown) in whichteleoperator manager 3607 is disposed, or from teleoperator computingdevice 3604. Examples of such signals include message data and areconfigured to provide information for purposes of monitoring autonomousvehicle 3630 by teleoperator 3608 (i.e., without requiring activeteleoperator intervention). Or, such signals may include a request forteleoperator 3608 to perform a task (e.g., provide guidance). In someexamples in which navigation-related teleoperation services is requestedby autonomous vehicle 3630, teleoperator manager 3607 may provide datarepresenting implicit guidance or explicit guidance. As to the latter,an example of explicit guidance includes actions relating to specificrouting paths (i.e., specific roadways or segments) that may betransmitted from teleoperator manager 3607 via network 3606 asinstruction data 3617. As to the former, examples of implicit guidanceinclude instructions to avoid or bypass certain geographic regions(e.g., suggested subset of roadways to deemphasize or exclude fromtrajectory calculations so as to guide autonomous vehicle 3630 away fromthese regions), as well as instructions specifying regions through whichtravel is preferred (e.g., suggested subsets of roadways or segments toemphasize or include (e.g., as a preference) in trajectory calculationsso as to guide autonomous vehicle 3630 toward these regions). Accordingto at least one example, any teleoperator 3608 may initiatecommunication or otherwise cause communication between teleoperatorcomputing device 3604 and autonomous vehicle 3630.

According to some examples, teleoperator manager 3607 may be invokedresponsive to message data 3619, which may include a request for onetype of many teleoperation services. For instance, the request forteleoperation service may include a request to negotiate difficult orunsafe roadway conditions and obstacles external to autonomous vehicle3630. An open door on a parked car that may be impeding travel byautonomous vehicle 3630 may be views as such an obstacle. Message data3619 may also include data representing an indication that, for example,observed lane markings are not located at expected positions in thephysical environment. Message data 3619 may also include a request forteleoperator services if, for instance, autonomous vehicle controller3647 experiences difficulties identifying or classifying one or moreobjects or obstacles that may affect planning and/or trajectorygeneration. Message data 3619 may also include a request to monitor theoperation of autonomous vehicle 3630 or any component therein (e.g.,sensor performance, drivetrain characteristics, battery charge levels,etc.). In some examples, message data 3619 may include information or arequest to address sensor-related events, such as a degraded or failedLidar sensor, radar sensor, or sonar sensor. Other examples includefaulty temperature measurements, degraded tachometer sensing on a drivetrain, etc. An example of degraded Lidar sensor may be due to intensesunlight or other light affecting range measurements. Message data 3619may also include information or a request to address measurementsderived from multiple sensors, such as kinematic-related data. Examplesof such measurements may include detected friction or wheel displacementthat varies relative to expected values of friction, anomalousacceleration measurement, etc. Note, too, that any autonomous vehicle infleet of autonomous vehicles 3630 a may generate message data 3619 a toestablish teleoperation services with teleoperator manager 3607, or toprovide autonomous vehicle 3630 teleoperation services. Theabove-described examples are not intended to be limiting, and, as such,message data 3619 may include any type of data, such as telemetry dataor planner data, that may be suitable for facilitating teleoperations.

In some cases, teleoperator manager 3607 may be configured invoketeleoperation services such that autonomous vehicle 3630 need notgenerate message data to establish a session between teleoperatorcomputing device 3604 and autonomous vehicle 3630. As shown,teleoperator manager 3607 may include a message data generator 3690 thatis configured to self-generate teleoperation service messages toestablish communications between teleoperator computing device 3604 andautonomous vehicle 3630. Diagram 3600 further depicts teleoperatormanager 3607 being communicatively coupled to a repository 3641 and to areference data repository 3605, which is shown to include map datarepository 3605 a. Repository 3641 may be configured as a policyrepository configured to store data representing policies or rules withwhich to determine when to invoke teleoperation services, based on anumber of factors that may affect trajectory generation by autonomousvehicle controller 3647, at least in some examples. For example, datastored in repository 3641 may include a rule to connect teleoperatorcomputing device 3604 to autonomous vehicles 3630 if the vehicle remainsstopped (e.g., beyond a time-related threshold) at a stop sign withoutany other detected obstacle or impediment that may be prohibiting travelthrough the stop sign. As another example, consider a rule that may beconfigured to invoke teleoperation services after a threshold amount oftime has been exceeded during which there is a failure to receive aperiodic signal (e.g., heartbeat signal) that confirms communication vianetwork 3606 is present. In yet another example, consider that map dataassociated with a geographic region through which autonomous vehicle3630 is predicted to travel is accessed by teleoperator manager 3607. Insome cases, such map data may be linked to alert data (“AD”) 3605 b thatindicates that one or more routes (e.g., in a map tile) may beassociated with certain levels of uncertainty or navigationalcomplexities. Or, in some cases, alert data may be linked to temporarymap data that is used temporarily for localization until, for example,the map data may be validated (e.g., via simulation, etc.). Therefore,alert data 3605 b causes instantiation of a teleoperations session toenable a teleoperator to monitor usage of such map data by autonomousvehicle 3630. When autonomous vehicle 3630 reaches a location relevantto map tiles linked to alert data 3605 b, teleoperator manager 3607 mayestablish a connection link between teleoperator computing device 3604and the vehicle. The above-described examples are not intended to belimiting, and, as such, message data generator 3690 may generate anytype of data suitable for facilitating teleoperations.

Diagram 3600 further depicts that teleoperator manager 3607 includes ateleoperator action controller 3612, which may be configured tofacilitate interactions between teleoperator 3608 and autonomous vehiclecontroller 3647 in autonomous vehicle 3630. Teleoperator actioncontroller 3612 is shown to include an action generator 3613 that may beconfigured to generate data to initiate one or more actions to observe,assist, monitor, etc. one or more aspects of autonomous vehicle 3630,responsive to, for example, input from teleoperator 3608. Actiongenerator 3613 may be configured to determine one or more courses ofactions (as one or more teleoperations services) to navigate orotherwise assist autonomous vehicle 3630. For example, action generator3613 may generate a subset of actions that may be undertaken by ateleoperator 3608 for navigating (or assisting in navigating) autonomousvehicle 3630 around an unsafe condition or hazardous obstacle. In thiscase, an action selected by teleoperator 3608 may cause autonomousvehicle 3630 to travel a particular route that includes one or more roadsegments over which to navigate around, for example, an event location(e.g., a geographic location or region in which an event may occur).

Further, action generator 3613 may be configured to generatenavigational data (e.g., as instruction data 3617) to guide autonomousvehicle 3630. Thus, action generator 3613 may identify routes or regionsof either preference or avoidance that may define data configured foruse (as instruction data 3617) by, for example, a planner (not shown) ofautonomous vehicle controller 3647. Action generator 3613 may further beconfigured to initiate a self-diagnostic tests (e.g., responsive toselection by teleoperator 3608) or calibration of subsets of sensors,communication devices, electrical components, electromechanicalcomponents, etc. to solicit additional information with whichteleoperator 3608 may use to ensure intended operability of autonomousvehicle 3630. Thus, teleoperator 3608 may communicate via teleoperatormanager 3607 with autonomous vehicle 3630 to evaluate operability of afailing sensor, and may further send instruction data 3617 to causeautonomous vehicle controller 3647 to diagnose (e.g., performdiagnostics, etc.), modify or correct operation of the sensor ofinterest. An action may also be represented by data configured to causepresentation of each view of a sensor (e.g., each individual set oflaser returns for each Lidar sensor) to assist teleoperator 3608 inmonitoring or diagnosing sensor performance issues. Another actiongenerated by action generator 3613 may include data representinginstructions to fetch sensor data from autonomous vehicle 3630associated with a past time interval so that, for example, teleoperator3608 may be able to visually review actions of autonomous vehicle 3630during a loss of communication (e.g., during which autonomous vehicle3630 self-navigates using a contingent trajectory to performs asafe-stop operation when principal trajectories are not available, suchas if operation of a planner degrades or fails).

According to various examples, an “event” relevant to teleoperationservices may be a condition or situation affecting operation, orpotentially affecting operation, of an autonomous vehicle (e.g., anevent may be associated with, or characterized by, a calculatedconfidence level). An event may be internal or external to autonomousvehicle 3630. For example, an obstacle obscuring a roadway may be viewedas an event, as well as a reduction or loss of communication. An eventmay include traffic conditions or congestion, as well as unexpected orunusual numbers or types of external objects (or tracks) that areperceived by a perception engine. An event may include weather-relatedconditions (e.g., loss of friction due to ice or rain) or the angle atwhich the sun is shining (e.g., at sunset), such as low angle to thehorizon that causes sun to shine brightly in the eyes of human driversof other vehicles. Bright sunlight may also impair operability ofsensors, such as laser or camera-based sensors. Accordingly, an eventmay be a condition associated with degraded performance of one or moresensors or failure thereof. An event may arise due to a rule violation(e.g., for a rule stored in repository 3641) or the detection of alertdata 3605 b linked to map data being used for localization. Theabove-described examples are not intended to be limiting, and, as such,these and other conditions may be viewed as events that may causeinvocation of a teleoperator service.

Diagram 3600 shows that teleoperator action controller 3612 alsoincludes a communication link manager 3615 that may be configured tomonitor and characterize attributes of communication links via networks3606 (e.g., bandwidth, identifiers for alternative communicationnetworks, such as various cellular networks and/or other wirelessnetworks, etc.). For example, communication link manager 3615 may detecta change in bandwidth, such as a reduction in bandwidth, and may adjustfunctionality of teleoperator manager 3607 to facilitate teleoperationsservice. To illustrate, consider a situation in which an amount ofbandwidth of a communication link via networks 3606 is reduced. Whengenerating a display of teleoperator communication device 3604, amountsof visualization data may be changed to enable teleoperator 3608 tovisually monitor a physical environment in which autonomous vehicle 3630travel. For example, communication link manager 3615 may causevisualization data to depict bounding boxes of dynamic and staticobjects in the physical environment rather than using sensor data havinggreater data amounts than the bounding boxes. Additionally,communication link manager 3615 may adjust bandwidth in accordance witha criticality of an event for which teleoperation service may assist.Examples of events of low criticality (e.g., low probability of causinginoperability) include minor deviations in performance of a sensor forwhich there are other redundant sensors, whereas examples of events ofhigh criticality (e.g., high probability of causing inoperability)include obstacles in a roadway. Further, communication link manager 3615may be configured to identify the presence or absence of communicationlinks among one or more autonomous vehicles 3630 and teleoperatormanager 3607, and may be configured to adapt teleoperation servicesaccordingly. For example, upon detecting a loss of a periodicconfirmation signal, teleoperator manager 3607 may alert teleoperator3608 of the event so that the teleoperator may seek alternate modes ofcommunication (e.g., generate actions by action generator 3613 toimplement other cellular networks, peer-to-peer (e.g., autonomousvehicle-to autonomous vehicle) networks, etc.).

Teleoperator manager 3607 of diagram 3600 also includes user interfacecontroller 3610, which may be configured to facilitate visualization anduser interface functionality. Visualization controller 3611 of userinterface controller 3610 is configured to process sensor-generated data(e.g., fused sensor data of one or more of Lidar sensors, cameras,radars, sonars, etc.) to generate high definition visualization data forpresentation on a display of teleoperator computing device 3604. In someexamples, visualization controller 3611 may implement one or moregraphics processing units (“GPUs”), including clusters of GPUs, tofacilitate generation of imagery of high fidelity with whichteleoperator 3608 may use to assist autonomous vehicle 3630. Accordingto some examples, visualization controller 3617 may generatevisualization data responsive to data from communication link manager3615. In particular, visualization controller 3611 may facilitate theuse of bounding boxes for presentation on a display rather than an imageof an object associated with the bounding box, if communication linkmanager 3615 indicates occurrence of an event of reduced availability ofbandwidth.

Input/output controller 3614 of user interface controller 3610 isconfigured to generate data representing user inputs and user outputs,both of which may be presented to a user interface or display ofteleoperator computing device 3604. Data signals associated with userinputs and user outputs may be exchanged between a user interface ofteleoperator computing device 3604 and teleoperator manager 3607. Suchdata signals may facilitate operation of any of the components ofteleoperator manager 3607 or any other component of an autonomousvehicle service platform to implement teleoperation services. Note thatelements depicted in diagram 3600 of FIG. 36 may include structuresand/or functions as similarly-named elements described in connection toone or more other drawings described herein.

FIG. 37 is a diagram depicting an example of a teleoperator managerconfigured to implement a user interface to facilitate teleoperationsservice commands, according to some examples. Diagram 3700 depictsteleoperator manager 3707 communicatively coupled via network(s) 3706with autonomous vehicle 3730, and being configured to implement userinterface 3701 to facilitate interaction with teleoperator. In theexample shown, consider that an event location 3703 indicates ageographic location, or a region thereof, at which one or moreautonomous vehicles may experience a level of complexity in determininga trajectory. A level of complexity may be expressed as a value (e.g.,numeric, probabilistic values, or any other suitable value), or a rangeof values, that may specify whether accuracy in determining trajectoriesmay be assisted with teleoperator services. In the example shown, eventlocation 3703 is depicted as being associated with a road network 3710(e.g., based on reference or map data, topographic data, traffic data,etc.) in user interface 3701. In this example, event location 3703 isshown to be located at an intersection or in the vicinity of a subset ofroad segments through which relatively large number of routes ortrajectories may be affected by event location 3703. Further to thisexample, event location 3703 may be a location at which there is aprobability that an event may occur or is occurring, such as a trafficaccident or an indication (e.g., based on alert data) that trajectorygeneration and route planning on autonomous vehicle 3730 may have acertain level of complexity or difficulty (e.g., at a particular time ofday, bright sunlight may hinder proper sensor operation). Thus,teleoperator manager 3707 may provide teleoperation services to assistautonomous vehicles in navigating event location 3703.

Diagram 3700 further depicts a variety of examples of accepting userinputs via user interface 3701 to facilitate any number of actions toguide navigation of autonomous vehicles 3730. In the following examples,autonomous vehicle 3730 may be displayed or visually represented aseither autonomous vehicle representation 3730 a or autonomous vehiclerepresentation 3730 b. According to an example shown, a teleoperator mayinfluence navigation of autonomous vehicle 3730 by applying userinput(s) to user interface 3701, whereby data signals representinginputs and outputs may be exchanged via any of one or moreinput-and-output devices, including, but not limited to, keyboards,mice, audio inputs (e.g., speech-to-text devices), graphical userinterfaces, displays, monitors, cursors, touch-sensitive displays, LCDor LED displays, and other I/O-related devices. As a representation ofan impending event location 3703 is displayed on user interface 3701, ateleoperator may interact with user interface 3701 to provide implicitguidance or explicit guidance to modify trajectories of autonomousvehicle 3730.

To illustrate commands implemented by a teleoperator, consider anexample in which user interface 3701 displays an autonomous vehiclerepresentation 3730 a and a teleoperator interacts with user interface3701 to assist in navigating autonomous vehicle 3730 in view of an eventassociated with that location 3703. The user input, such as user input3729 a (e.g., via cursor or touch-screen input), may set a navigationpoint, “D,” which may be used to provide implicit guidance. In thisexample, navigation point D may represent a point associated with ageographic region in road network 3710 that may influence planning andtrajectory generation at autonomous vehicle 3730. Navigation point D maybe configured to generate trajectories either toward navigation point D(i.e., as an attract point) or away from navigation point D (i.e., as arepel point). According to some examples, the degree of attracting orrepelling may have values that vary linearly or non-linearly from thedistance, r, between vehicle 3730 a and navigation point D. For example,if navigation point D is configured as a repel point, a planner atautonomous vehicle 3730 may generate trajectories to determine (e.g.,probabilistically) that road segment 3725 is less likely to be includedin generated trajectories than road segment 3727, which is at a furtherdistance from navigation point D than road segment 3725.

Further to the example shown, a teleoperator may establish a boundary toeither attract or repel trajectory generation for autonomous vehicle3730 either toward or away from the boundary. As shown, user input touser interface 3701 may establish a repel boundary 3794 betweennavigation points A and B, which generates input signal data thatteleoperator manager 3707 processes to form instruction data 3717,which, in turn, is used in trajectory generation processes of autonomousvehicle 3730 to avoid repel boundary 3794. Note, too, addition ofnavigation point C may establish a repel region 3792 that encapsulatesroad segments 3721 and 3723, both of which may be deemphasized in, orexcluded from, trajectory generation so as to bypass event location3703. In some cases, repel region 3792 may be configured as a “no-travelzone” in which travel is prohibited. In other instance, repel boundary3794 and repel region 3792 may be formed as an attract boundary and anattract region, respectively, if a teleoperator desires that autonomousvehicle 3730 navigates to event location 3703 (e.g., via road segment3721 or road segment 3723). According to some examples, user input 3729b may include continuous inputs (or semi-continuous inputs) as a set ofnavigation points formed, for example, when a teleoperator swipes afinger across a surface of user interface 3701. As shown, user input3729 b forms an attract region 3791, which is configured to influencetrajectory generation toward road segments associated with or in thevicinity of attract region 3791. In various examples, theabove-described navigation points, boundaries, and regions may beapplied to a specific autonomous vehicle or to multiple autonomousvehicles (e.g., a fleet of autonomous vehicles, including an autonomousvehicle associated with autonomous vehicle representation 3730 b).

Diagram 3700 further depicts examples of accepting user inputs via userinterface 3701 to provide navigational guidance to autonomous vehicles3730, which may be presented via user interface 3701 to a teleoperatoras autonomous vehicle representation 3730 b. Teleoperator manager 3707may be configured to accept user inputs to invoke one or more actionsvia user interface 3701 by generating instruction data 3717 that may betransmitted to autonomous vehicle 3730. In an instance in whichautonomous vehicle 3730 is displayed as autonomous vehiclerepresentation 3730 b, teleoperator manager 3707 may also be configuredto cause presentation of user inputs 3731 a, 3732 a, and 3733 a, whichcorrespond to a first action (“1”) 3731, a second action (“2”) 3732, anda third action (“3”) 3733, respectively, for presentation on userinterface 3701. Action 3731 may include a first subset of instructionsthat causes autonomous vehicle representation 3730 b, and thusautonomous vehicle 3730, to turn left at a first street. Action 3732 mayinclude a subset of instructions that causes autonomous vehiclerepresentation 3730 b (and autonomous vehicle 3730) to turn left at asecond street. Action 3733 may include a subset of instructions thatcauses autonomous vehicle representation 3730 b (and autonomous vehicle3730) to turn right at the second street. Note that actions 3731 to 3733are configured to provide a teleoperator-guided route to causeautonomous vehicle 3730 b to bypass event location 3703. According tosome examples, user inputs 3731 a, 3732 a, and 3733 a may be configured,as instruction data 3717, to override at least one planner-generatedtrajectory.

As shown, user interface 3701, or a portion thereof, may present, one ormore selectable courses of action 3741 with which a teleoperator mayimplement to assist navigation of autonomous vehicle 3730. In thisexample, a first user input (“action 2”) 3732 a to implement action3732, a second user input (“action 1”) 3731 a to implement action 3731,and a third user input (“action 3”) 3733 a to implement action 3733.User inputs 3731 a, 3732 a and 3733 a may represent interactive portionsof user interface 3701 that activate respective data signals—as inputsignals—responsive to, for example, a user input 3729 c, which may be afinger touching a specific portion of user interface 3701. According tosome examples, a planner of autonomous vehicle 3730 may transmit datarepresenting these selections as message data 3719 to teleoperatormanager 3707, as the planner may identify optimal actions and/ortrajectories for presentation to, and selection by, a teleoperator inview of an event. Further, user inputs 3732 a, 3731 a, and 3733 a eachare displayed with a corresponding ranking 3742 of 0.98, 0.96, and 0.89,respectively, specifying a relative preference or recommendation forselecting an appropriate user input. Note, however, any indicia otherthan ranking may be used. According to some examples, user inputs 3731a, 3732 a and 3733 a may presented as a weighted set of potentialsolutions as actions (e.g., probabilistically-weighted). In someexamples, a planner of autonomous vehicle 3730 may provide rankings 3742as message data 3719, whereby rankings 3743 may indicate a probabilityof successful navigation of a proposed route or trajectory, ascalculated by the planner. Note that the above-described ranking 3742 isnot limited to probabilities or the numbers shown, and may be of anyform of distinguishing a recommended selection relative to otherrecommended selections. Based on ranking value 3742 of 0.98, ateleoperator (i.e., user input 3729 c) may select user input 3732 a toeffect action 3732.

According to some examples, simulator 3740 may receive message data 3719as well as any other data from any component of an autonomous vehicleservice platform and a fleet of autonomous vehicles to simulateimplementation of actions 3731 to 3733 to determine, in real-time (ornear-real time) for autonomous vehicle 3730 (or subsequent autonomousvehicles 3730 encountering event location 3703), whether a specificranking value 3742 is optimal. For example, simulator 3740 may generate,as simulation data 3718, simulation values 3744 for display with acorresponding user input. In this example, a simulation value of “1” mayindicate agreement with an associated ranking, whereas lesser values ofsimulation value 3744 may indicate a greater divergence from ranking3742 that a teleoperator may wish to consider before relying on rank3742. In the example shown, rank value 3742 of user input 3731 a isassociated with a simulation value 3744 of “1,” and, as such, ateleoperator may select user input 3731 a rather than user input 3732 a.According to some examples, simulator 3740 may be configured to simulatethe operability of autonomous vehicle 3730 in a virtual environment,teleoperator interactions with user interface 3701, and other functionsof an autonomous vehicle service platform in which teleoperator manager3707 may be implemented to, among other things, validate thefunctionality and structure of teleoperator manager 3707.

According to various examples, the implementation of rank values 3742and/or simulator values 3744 need not be limited to those describedabove, but may include any other values or input types fordistinguishing optimal courses of actions that teleoperators may select.Selectable courses of action 3741 may be used with providing eitherimplicit or explicit guidance, and may include other actions other thannavigation-related actions. Examples of other actions include actions totest or view sensor data, actions to self-calibrate or self-diagnoseissues on board autonomous vehicle 3730, actions to communicate withoccupants of vehicle 3730, and any other relevant action. User inputs,such as those shown as selectable courses of actions 3741, as well asuser inputs configured to implement navigation points, boundaries,regions, and the like, may be implemented as preemptive courses ofactions, whereby autonomous vehicle 3730 may receive teleoperationservices timely such that operations of autonomous vehicle 3730 need notbe affected or degraded by, for example, an event or at event location3703. For example, autonomous vehicle 3730 need not stop or modify itsperformance prior to being impacted by an event. Note that elementsdepicted in diagram 3700 of FIG. 37 may include structures and/orfunctions as similarly-named elements described in connection to one ormore other drawings described herein.

FIG. 38 is a diagram depicting another example of a teleoperator managerconfigured to implement a user interface to facilitate teleoperationsservice commands, according to some examples. Diagram 3800 depictsteleoperator manager 3807 communicatively coupled via network(s) 3806with autonomous vehicle 3830, and being configured to implement userinterface 3801 to facilitate interaction with a teleoperator. In theexample shown, teleoperator manager 3807 is configured to present, viauser interface 3801, a view 3810 (e.g., a 3D view) from at least oneleading surface of an autonomous vehicle in a direction of travel in aphysical environment (e.g., roadway, cityscape, etc.) in whichautonomous vehicle 3830 travels. According to some examples, datarepresenting view 3810 may be transmitted from autonomous vehicle 3830as message data 3819, and the data representing view 3810 may includeoptimized amounts of data (e.g., as a function of bandwidth ofcommunication channels in networks 3806, etc.), such as sensor data (orany amount less) that may generate visualization data of any resolutionso that a teleoperator may provide teleoperation commands based on, forexample, events depicted view 3810. Teleoperator manager 3807 may befurther configured to present, via user interface 3801, selectablecourses of action 3841, a message log 3841 a based on message data 3819,and an autonomous vehicle (“AV”) identifier 3881 a indicating thespecific autonomous vehicle 3830 associated with user interface 3801.

In this example, consider that a teleoperator associated with userinterface 3801 is presented with event-related information based onevents autonomous vehicle 3830 may be experiencing. A teleoperator mayreceive message (“1a”) 3843 a representing an “immobile alert,” whichmay indicate that autonomous vehicle 3830 is in encountering one or moreevents (e.g., associated with a range of confidence levels) for whichteleoperation services may be invoked. In some cases, an immobile alertindicates that autonomous vehicle 3830 has been immobile beyond athreshold of time for which there may not be determinable reason (e.g.,an autonomous vehicle controller of autonomous vehicle may not be ableto generate acceptable trajectories in view of the events that mayprevent safe or unobstructed travel). Here, one or more traffic cones3861 may be detected as obstacles, as well as an occurrence in which acar door 3869 (of a parked car) extends, as an obstacle, into a spatialregion in which generated trajectories are predicted to traverse. Insome cases, an autonomous vehicle controller of autonomous vehicle 3830may be configured to detect the above-described obstacles prior toarriving at the geographic location associated with view 3810, whereby ateleoperator may intervene preemptively to provide navigation guidanceas instruction data 3817 to cause the vehicle to following path 3866.With preemptive guidance from a teleoperation, operation of autonomousvehicle 3830 need not be affected by obstacles (or minimally affected).In other cases, car door 3869 may instantaneously open such thatautonomous vehicle 3830 detects the car door opening and stops to awaitits closure. If an excessive time at which the vehicle is immobile isdetected, then message data 3819 may include message 3843 a.

In another example, consider that teleoperator manager 3807 causesmessage (“1b”) 3843 b to be displayed to indicate to a teleoperator thatcommunications are lost with autonomous vehicle 3830. For example,teleoperator manager 3807 determines that an autonomous vehicle serviceplatform fails to receive either sufficient data or a periodic signal(e.g., heartbeat signal) that confirms communication via network 3806 ispresent. A teleoperator may, via user interface 3801, initiate one ormore actions to reestablish communications. For example, a teleoperatorcomputing device may be configured to establish an alternatecommunication link (e.g., via peer-to-peer networking, alternatecellular networks, etc.) with autonomous vehicle 3830. Further to theabove-described example, consider that communications are reestablished,and message (“2b”) 3843 c is generated to indicate same to ateleoperator. In this case, a teleoperator viewing user interface 3801may perceive that autonomous vehicle 3830, whether due to loss ofcommunications and/or other reasons, had implemented a safe-stoptrajectory 3892 to stop autonomous vehicle at a safe region 3890. Also,the teleoperator may view a message (“3b”) 3843 d indicating thatautonomous vehicle 3830 is, for example, implementing (or hasimplemented) a safe-stop recovery mode or routine, which may beoptional, to establish that autonomous vehicle 3830 is functionallyand/or structurally able to continue transiting. For example, asafe-stop recovery routine may be configured to confirm that subsets ofsensors are operating in respective ranges of normative operatingparameters. As such, autonomous vehicle 3830 may be able to resumeautonomous vehicle services upon completion of successful safe-stoprecovery processes.

Further, a teleoperator may provide user input 3829 a to select oractivate a user input (“action 1”) 3831 a, which may initiate aself-test routine by autonomous vehicle 3830 to validate properoperation of its systems. A teleoperator may also select a user input(“action 2”) 3832 a to cause autonomous vehicle 3830 to performself-calibration processes on one or more sensors or other components.In some cases, a teleoperator may select a user input (“action 3”) 3833a to cause a replay of sensor data subsequent to the loss ofcommunications and prior to the safe-start operation to determine, forexample, there were no other events, such as autonomous vehicle 3830striking or otherwise encountering a basketball bouncing into thestreet. A teleoperator may provide instruction data 3817 to autonomousvehicle 3830 to travel one or more guided trajectories 3866.

Simulator 3840, according to some examples, may be configured tosimulate the operability of autonomous vehicle 3830 as virtualautonomous vehicle 3830 a in a virtual environment. As such, virtualautonomous vehicle 3830 a may generate simulated message data 3819 a.Note that simulator 3840 may also be configured to simulatefunctionalities of teleoperator manager 3807, teleoperator interactionswith user interface 3801, and other functions of an autonomous vehicleservice platform in which simulator 3840 may be implemented to, amongother things, simulate and validate the functionality and structure ofteleoperator manager 3807.

In some instances, teleoperator manager 3807 may cause visualizationdata to adjust content for presentation by, for example, depicting oneor more obstacles as bounding boxes 3863 in the depiction of thephysical environment rather than using sensor data having greater dataamounts than the bounding boxes. As such, less data may be transmittedvia message data 3819 to form view 3810. Or, in some cases, detectedobjects or features of the physical environment which sufficientlydeviate from map data may be transmitted as message data 3819 ratherthan, for example, transmitting data associated with static-relatedobject or previously-detected objects. In some examples, a user 3802(e.g., a R&D developer or passenger) may interact with a computingdevice 3803 (e.g., a mobile computing device) to generate message data3819 or to cause autonomous vehicle 3830 to generate such data. Notethat elements depicted in diagram 3800 of FIG. 38 may include structuresand/or functions as similarly-named elements described in connection toone or more other drawings described herein.

FIG. 39 is a diagram depicting yet another example of a teleoperatormanager configured to implement a user interface to facilitateteleoperations service commands, according to some examples. Diagram3900 depicts teleoperator manager 3907 communicatively coupled vianetwork(s) 3906 with autonomous vehicle 3930, and being configured toimplement user interface 3901 to facilitate interactions with ateleoperator. In the example shown, teleoperator manager 3907 isconfigured to present, via user interface 3901, views 3910 (e.g., 3Dviews) as specific views of portions of physical environment, wherebyeach of views 3910 may relate to a sensed portion of the environment asgenerated by a corresponding sensor. According to some examples, datarepresenting view 3910 may be transmitted from autonomous vehicle may beused to notify a teleoperator of sensor operations so that theteleoperator may provide teleoperation commands based on, for example,events depicted with respect to views 3910. Teleoperator manager 3907may be further configured to present, via user interface 3901,selectable courses of action 3940, a message log 3941 based on messagedata 3919, and an autonomous vehicle (“AV”) identifier 3981 a indicatingthe specific autonomous vehicle 3930 associated with user interface3901.

In the example shown, consider that a teleoperator receives a message(“1”) 3943 a indicating that a sensor is operating in a manner that atleast one parameter may be operating within a range of marginalparametric values. In some situations, views 3910 need not be presentedon user interface 3901 until a teleoperator provides user input 3929 ato select user input (“action 1”) 3931 a to cause sensor data to beviewed on user interface 3901. Further, a teleoperator may receive viauser interface 3901 a message (“2”) 3943 b specifying a sensor alert,whereby at least one sensor parameter value may be deviating from atleast one range of normative parametric values. As such, an event may bedetected as the degradation of sensor functionality. Upon implementationof user input 3931 a, instruction data 3917 is transmitted to autonomousvehicle 3930, which, in response, is configured to transmit sensed datafrom, for example, sensors 1 to 4 as message data 3919 to teleoperatormanager 3907, which causes presentation of sensor data views 3970, 3972,3974, and 3976. As shown, degradation of a sensor, such as sensor 1, maybe depicted visually as an anomaly 3977 (e.g., a physical defect insensor, an obstruction on the sensor, too much intense light, etc.).Thus, a teleoperator may visually confirm the presence of an anomaly insensor operation. The teleoperator may select user input (“2”) 3932 aand user input (“3”) 3933 a to perform sensor calibration and sensorreorientation, respectively, by transmitting such instructions asinstruction data 3917 to autonomous vehicle 3930. An instructionresponsive to user input may cause autonomous vehicle 3930 to modify itsoperation to reduce or negate sensor degradation. An example ofreorienting a sensor of interest (e.g., sensor 1) is described in FIG.3E, 25 among other examples described herein.

According to various examples, views for more or fewer sensors thanthose shown may be presented on user interface 3901. Further, any typeof sensors, such as Lidars, cameras, radars, sonars, etc., may provideany type of data for presentation as view 3910. Note, too, that views3970, 3972, 3974, and 3976 are not limited to the above-describedsensors, and teleoperator manager 3907 may be configured to generate anyvisual representation of sensed parameters, such as temperature, batterycharge, wheel angles, speed, etc.

Simulator 3940, according to some examples, may be configured tosimulate the operability of sensors in autonomous vehicle 3930 asvirtual sensors used in a virtual environment, along with a virtual orsynthetic version of autonomous vehicle 3930. Note that simulator 3940generates at least a portion of data may also be configured simulatefunctionalities of teleoperator manager 3907, teleoperator interactionswith user interface 3901, and other functions of an autonomous vehicleservice platform in which simulator 3940 may be implemented to, amongother things, simulate and validate the functionality and structure ofsensors in autonomous vehicle 3930 using teleoperator manager 3907. Notethat elements depicted in diagram 3900 of FIG. 39 may include structuresand/or functions as similarly-named elements described in connection toone or more other drawings described herein.

FIG. 40 is a flow chart illustrating an example of performingteleoperation services, according to some examples. Flow 4000 beginswith 4002 at which a teleoperations message may be received viacommunication link between, for example, an autonomous vehicle and anautonomous vehicle service platform. In some examples, theteleoperations message may include sensor data (or other data)configured to provide a depiction of a physical environment in which theautonomous vehicle is traveling. For example, the depiction may bepresented to a user interface and may be formed with fused sensor data.At 4004, data specifying event associated with an autonomous vehicle isdetected based on message data. At 4006, one or more courses of actionsmay be identified. At least one action may be selected for performanceresponsive to detecting the event. At 4008, visualization data isgenerated to present information associated with the event via a userinterface to a teleoperator interacting with a teleoperator computingdevice. The visualization data may be presented to a display or userinterface so that a teleoperator is presented with the visualizationdata to determine whether to intervene in operation of a planner (e.g.,trajectory generation) of an autonomous vehicle. Note that the orderdepicted in this and other flow charts herein are not intended to implya requirement to linearly perform various functions as each portion of aflow chart may be performed serially or in parallel with anyone or moreother portions of the flow chart, as well as independent or dependent onother portions of the flow chart.

FIG. 41 illustrates examples of various computing platforms configuredto provide various teleoperations-related functionalities and/orstructures to components of an autonomous vehicle service, according tovarious embodiments. In some examples, computing platform 3300 may beused to implement computer programs, applications, methods, processes,algorithms, or other software to perform the above-described techniques.Note that one or more structures and/or functionalities of FIG. 33 maybe applicable to FIG. 41, and, as such, some elements in those figuresmay be discussed in FIG. 33. Note further that elements depicted indiagram 4100 of FIG. 41 may include structures and/or functions assimilarly-named elements described in connection to one or more otherdrawings herein.

Referring to the example shown in FIG. 41, system memory 3306 includesan autonomous vehicle service platform module 4150 and/or its components(e.g., a teleoperator services module 4152, etc.), any of which, or oneor more portions of which, can be configured to facilitate navigationfor an autonomous vehicle service by implementing one or more functionsdescribed herein.

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described inventivetechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described inventiontechniques. The disclosed examples are illustrative and not restrictive.

1-18. (canceled)
 19. An autonomous vehicle comprising: one or moreprocessors; a communication interface, the communication interfaceconfigured to establish a communications link between the autonomousvehicle and one or more other devices or autonomous vehicles; one ormore sensors; and memory having stored thereon processor-executableinstructions that, when executed by the one or more processors,configure the autonomous vehicle to perform operations comprising:obtaining first sensor data from at least one of the one or moresensors, the first sensor data generated during a first time period;determining, by the one or more processors, that an event has occurredbased at least on the first sensor data; determining, by the one or moreprocessors, an event type based at least on the first sensor data;request, via the communication interface, a teleoperator command basedat least in part on the event type; receiving, via the communicationinterface, the teleoperator command; and controlling the autonomousvehicle based at least in part on the teleoperator command.
 20. Theautonomous vehicle of claim 19, wherein the operations further comprise:adjusting, at the communication interface, an available bandwidth of thecommunications link for transmitting the first sensor data via thecommunications link; determining an adjusted amount of the first sensordata to be transmitted based at least in part on the availablebandwidth; and transmitting, via the communications interface, theadjusted amount of sensor data.
 21. The autonomous vehicle of claim 20recites, wherein: adjusting the available bandwidth includes decreasingthe available bandwidth based at least in part on the event type; anddetermining the adjusted amount of the sensor data to be transmittedincludes determining to transmit less than all of the sensor data. 22.The autonomous vehicle of claim 20, wherein determining to transmit lessthan all of the sensor data includes one or more of: determining totransmit a representation of at least a portion of the first sensordata, determining to transmit less than all of the first sensor data,determining to transmit the first sensor data in a reduced resolution,determining to transmit a subset of the first sensor data correspondingto less than all of the one or more sensors, or determining to transmita heartbeat signal instead of the sensor data.
 23. The autonomousvehicle of claim 22, wherein the representation comprises one or more ofa bounding box or metadata associated with an object, the objectdetermined based, at least in part, on the first sensor data.
 24. Theautonomous vehicle as claim 19 recites, wherein the teleoperator commandcomprises instructions to control the autonomous vehicle to run one ormore of a self-diagnosis, repair, or calibration of the one or moresensors.
 25. The autonomous vehicle of claim 19, the operations furthercomprising: receiving second sensor data from at least one of the one ormore sensors, the second sensor data generated at a second time period,wherein the second time period occurs chronologically before the firsttime period, wherein the second time period occurs during a loss of thecommunication link, and wherein determining the event has occurred isadditionally based, at least in part, on the second sensor data.
 26. Theautonomous vehicle as claim 19 recites, the operations furthercomprising: determining an event confidence level that indicates aprobability that the event will impact operation of the autonomousvehicle, the event type including a criticality of the event based atleast in part on the event type, and wherein determining the event typeincludes one or more of: determining that the autonomous vehicle hasbeen immobile for a threshold amount of time, determining that theautonomous vehicle is located within a region associated with alertdata, determining that the event confidence level is below a threshold,or determining that previously determined event confidence levelsexhibit a decreasing trend.
 27. The autonomous vehicle as claim 19recites, the operations further comprising: determining an attribute ofa communications link established by the communication interface; andadjusting, at the communication interface, the available bandwidth ofthe communications link for transmitting the first sensor data via thecommunications link based at least in part on one or more of theattribute or the event type.
 28. The autonomous vehicle of claim 19,wherein the teleoperator command is based at least in part on one ormore of: a teleoperator suggestion, the teleoperator suggestion based,at least in part, on a suggestion confidence level, the suggestionconfidence level indicating a probability that the teleoperatorsuggestion mitigates the event, or a simulator suggestion, the simulatorsuggestion indicating that a corresponding teleoperator suggestion runon a simulator mitigates the event.
 29. A method comprising: obtainingsensor data from a sensor, the sensor located on an autonomous vehicle;determining that an event has occurred based at least on the sensordata; determining an event type and an event confidence level from atleast the sensor data, wherein determining the event type includes oneor more of: determining that the autonomous vehicle has been immobilefor a threshold amount of time, determining that the autonomous vehicleis located within a region associated with alert data, or determiningthat the event confidence level is below a threshold; requesting, via acommunication interface, a teleoperator command based at least in parton the event type; receiving the teleoperator command; and controllingthe autonomous vehicle based at least in part on the teleoperatorcommand.
 30. The method of claim 29, wherein the communicationsinterface is configured to establish a communications link between theautonomous vehicle and one or more other devices or autonomous vehicles,the communications link having an available bandwidth, and wherein themethod further comprises directing the autonomous vehicle to transmit atleast a portion of the sensor data via the communications interface. 31.The method of claim 30 further comprising directing the autonomousvehicle to adjust the available bandwidth based, at least in part, onone or more of the event, the event type, or the event confidence level.32. The method of claim 31 further comprising directing the autonomousvehicle to adjust an amount of the sensor data to transmit via thecommunications interface based, at least in part, on one or more of theadjusted available bandwidth, the event type, or the event confidencelevel.
 33. The method of claim 31, wherein directing the autonomousvehicle to adjust an amount of the sensor data to transmit via thecommunications interface includes one or more of: determining totransmit less than all the sensor data; determining to transmit thesensor data in a reduced resolution; determining to transmit arepresentation of at least a portion of the sensor data instead of allthe sensor data; determining to transmit a subset of the sensor datacorresponding to a subset of sensors, the sensor being one of aplurality of sensors and the subset of sensors being less than all ofthe plurality of sensors; or determining to transmit a heartbeat signalinstead of the sensor data.
 34. The method of claim 29, wherein theteleoperator command is based at least in part on one or more of: ateleoperator suggestion, the teleoperator suggestion based, at least inpart, on a suggestion confidence level, the suggestion confidence levelindicating a probability that the teleoperator suggestion mitigates theevent, or a simulator suggestion, the simulator suggestion indicatingthat a corresponding teleoperator suggestion run on a simulatormitigates the event.
 35. The method of claim 30, wherein at least aportion of the sensor data is obtained during a period of time in whichthe communication link is lost.
 36. One or more non-transitorycomputer-readable media storing computer-executable instructionsconfigured to program one or more computing devices to performoperations comprising: receiving sensor data from a sensor, the sensorlocated on an autonomous vehicle; determining that an event has occurredbased at least on the sensor data; determining an event type from atleast the sensor data; establishing a communications link, by acommunication interface, between the autonomous and one or more ofanother device or another autonomous vehicle; setting an availablebandwidth of a communications link based at least in part on or more ofthe event or the event type transmitting at least a portion of thesensor data via the communications interface, the portion of the sensordata selected to not exceed the available bandwidth; requesting, via thecommunication interface, a teleoperator command based at least in parton the event type; receiving, via the communication interface, theteleoperator command; and controlling the autonomous vehicle based, atleast in part, on the teleoperator command.
 37. The one or morenon-transitory computer-readable media of claim 36, wherein the portionof the sensor data includes one or more of: a heartbeat signal, lessthan all the sensor data, the sensor data in a reduced resolution,sensor data from less than all of a plurality of sensors, or arepresentation of at least a portion of the sensor data.
 38. The one ormore non-transitory computer-readable media of claim 36, whereinreceiving sensor data comprises receiving sensor data during a period oftime during which the communication link is lost.
 39. The one or morenon-transitory computer-readable media of claim 36, wherein theteleoperator command is based at least in part on one or more of: ateleoperator suggestion, the teleoperator suggestion based, at least inpart, on a suggestion confidence level, the suggestion confidence levelindicating a probability that the teleoperator suggestion mitigates theevent, or a simulator suggestion, the simulator suggestion indicatingthat a corresponding teleoperator suggestion run on a simulatormitigates the event.
 40. The one or more non-transitorycomputer-readable media of claim 36, wherein controlling the autonomousvehicle comprises controlling the autonomous vehicle to perform one ormore of a calibration of the one or more sensors, a self-diagnosis, or arepair.